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Indo-US Workshop on Essential Algorithms for Integrated Vehicle Health Management for Aerospace Applications

23-26 May 2016 Indian Institute of Science, Bangalore, India

Technical Program

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The workshop will be held at the Department of Aerospace Engineering which is one of the oldest centers for aerospace research in India. The department is located in the green campus of IISc in the heart of Bangalore and well connected to airport, train and bus stations.

Related information: Host Institution: Indian institute of Science www.iisc.ernet.in US Partner Institution: Vollanova University www.villanova.edu Workshop Sponsor: Indo-US Science and Technology Forum (IUSSTF) www.iusstf.org Workshop website: http://www.aero.iisc.ernet.in/~imemslab/web/IVHMWorkshop/IVHMMainpage.html

Workshop Venue: Auditorium, Department of Aerospace Engineering, Indian Institute of Science

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Aerospace systems are complex and can fail due to a variety of reasons. Early prediction and prevention of these failures pose great challenges. The evolution of faults in such complex systems is highly nonlinear and remains quite unpredictable. It is becoming increasingly clear that robust predictions are only possible with a fundamental insight into the underlying science of the system represented through variables interacting across various physical, data and information elements and external factors. Such representations, in the form of mathematical models and algorithms, are emerging today as the key ingredients in advancing and realizing Integrated Vehicle Health Management (IVHM) systems. A robust and reliable IVHM system would extend the Remaining Useful Life (RUL) of the system and enhance energy efficiency while maximizing operational ease and safety. Mathematical modelling of physical, data, information processes and the resulting algorithms are essential to solve various groups of problems. Certain groups set functional objectives related to aerospace systems operation which have to be monitored to find behavioural changes; certain groups pose the problem of detecting and studying hidden faults in the system; and, certain other groups pose the problem of finding out the cause and effect behind behavioural changes and the hidden faults and to take early remedial action. The management practices combining all these elements are crucial as well, and should also be optimized and monitored with the help of algorithmic tools and techniques in information and data science, and with full cognizance of business, policy and regulatory practices. The workshop is a follow-up of WIAS 2012 workshop on IVHM organized by the National Aerospace Laboratories Bangalore in 2012.

WIVHM 2016 aims to bring together academic researchers and practicing engineers working in the field of algorithms for IVHM. In the backdrop of various different scenarios emerging in aerospace R&D and industries in India and US, the workshop will focus on reviewing state of the art developments in algorithms for (1) Electrical, electronics, communication and control systems health management (EECCS-HM) (2) Machine, mechanism and interface health management (MMI-HM) (3) Airframe health management (AF-HM) (4) Low resource setting and optimization (LRSO) (5) Vehicle level reasoning, systems software and learning (VLRSSL)

Emerging problems in algorithm development will be studied in few small groups during the workshop followed by discussion on networking and collaboration.

The workshop invites advanced level graduate students and young researchers working in the fields of mathematical modeling and algorithms to participate in the thematic group meetings during the workshop. Further details will be communicated to interested participants. Alternatively, interested graduate students and researchers interested in the workshop can contact the workshop organizers by e-mail. Apart from the group meetings, the open sessions of the workshop will cover talks by invited speakers from the US and India.

D. Roy Mahapatra (Indian Coordinator), Indian Institute of Science, Bangalore, India C. 'Nat' Nataraj (US Coordinator), Villanova University, Villanova, USA K. Vijayaraju Aeronautical Development Agency, Bangalore, India VanamUpendranath National Aerospace Laboratories, Bangalore, India Kai Goebel NASA Ames Research Center, CA, USA

Invited Speakers 24 Thematic Groups 5 Students participating in group meetings 17 Academic Faculty/Researchers participating in group meetings 14 Industry participants 15 Total number of participants 75

Background and Objectives of the Workshop

Organizing Committee

Workshop at a Glance

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Each invited talk duration 45 minutes followed by 15 minutes Q&A

23 May 2016 07:30-08:45 Breakfast (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 08:00-08:45 Registration (Auditorium Lobby, Aerospace Engineering Department) 08:45-09:00 Opening Remarks (Workshop Coordinators: C. Nataraj / D. Roy Mahapatra) Panel Remarks – All speakers for the day (Modeling and Analysis Paradigm)

Moderator – C. Nataraj / GautamBiswas 09:00-10:00 On Algorithms and Architectures for IVHM: Experiences from Across Domains – Part I

Siddhartha Mukhopadhyay 10:00-11:00 On Algorithms and Architectures for IVHM: Experiences from Across Domains – Part II

AmitPatra 11:00-11:15 Tea Break (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 11:15-12:15 Simplified Damage Models and detection Methodologies for SHM of Metallic and Composite

Structures S. Gopalakrishnan

12:15-13:15 TBD 13:15-14:15 Lunch (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 14:15-15:15 Group Meeting 15:15-15:30 Tea Break (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 15:30-16:30 Challenges in Aircraft Health Monitoring

Michael H.Azarian 16:30-17:30 Aerospace IVHM projects at CSIR-NAL: Lessons Learned

VanamUpendranath 19:30-21:30 Dinner (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department)

24 May 2016 07:30-08:45 Breakfast (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 08:45-09:00 Panel Remarks – All speakers for the day (Subsystems data and signal complexity) Moderator – Siddhartha Mukhopadhyay / AmitPatra

Program Schedule

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09:00-10:00 Distributed Hierarchical FDI for System-Level Health Management RajagopalanSrinivasan

10:00-11:00 Gas Path Health Monitoring of Legacy Aero-Engine: Beginning

Soumendu Jana 11:00-11:15 Tea Break (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 11:15-12:15 A Smart Platform for Prognostics and Health Management of Rotating Machinery

Dinesh Nair 12:15-13:15 Open challenges in big data analytics for engineering systems

VinayRamanath 13:15-14:15 Lunch (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 14:15-15:15 Group Meeting 15:15-15:30 Tea Break (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 15:30-16:30 Neural networks, Kalman filters and Fuzzy Logic in Health Monitoring of Aerospace Systems

RanjanGanguli 16:30-17:30 Hybrid algorithms for dynamic system diagnostics,

C. Nataraj 19:30-21:30 Dinner (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department)

25 May 2016 07:30-08:45 Breakfast (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 08:45-09:00 Panel Remarks – All speakers for the day (Complex Systems, Solution Framework) Moderator – Vinay Jammu / Michael H. Azarian 09:00-10:00 Combining Model- and Data-Driven Methods for Diagnostics and Prognostics of Complex

Systems GautamBiswas

10:00-11:00 Reliability and Availability Modeling in Practice

KishorTrivedi 11:00-11:15 Tea Break (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 11:15-12:15 Driving Efficiencies through Connected Smart Products

G. V. V. Ravi Kumar 12:15-13:15 Spacecraft Mission Planning, Management, Fault Diagnostics Algorithms and Prevailing

Procedures RamalingamPandiyan

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13:15-14:15 Lunch (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 14:15-15:15 Group Meeting 15:15-15:30 Tea Break (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 15:30-16:30 (TBD)

AshaGarg 16:30-17:30 Damage detection & health monitoring of integrated composite aircraft structures

S. R. Viswamurthy 17:30-18:00 Simulation driven learning of fault diagnostics

D. Roy Mahapatra 19:30-22:30 Banquet Dinner (Royal Orchid Hotel)

26 May 2016 07:30-08:45 Breakfast (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 08:45-09:00 Panel Remarks –All Speakers for the day (Total system, Operational Perspective) Moderator – KishorTrivedi / K Vijayaraju 09:00-10:00 Airplane Health Management

Seema Chopra 10:00-11:00 Digital Twin

Vinay Jammu 11:00-11:15 Tea Break (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 11:15-12:15 (TBD)

Nalinaksh S. Vyas 12:15-13:15 Evolving a National initiative for IVHM in Indian aeronautical sector

Prakash D. Mangalgiri 13:15-14:15 Lunch (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 14:15-15:15 Realizing IVHM technologies for the total aircraft

Kota Harinarayana 15:15-15:30 Tea Break (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department) 15:30-16:30 Group Meeting Review 16:30-17:30 Panel Discussion and Closure 19:30-21:30 Dinner (2nd Floor Roof-top, Auditorium building, Aerospace Engineering Department

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USA C.Nataraj Villanova University, USA Dinesh Nair National Instruments, USA GautamBiswas Vanderbilt University, USA Kishore Trivedi Duke University, USA Michael Azarian University of Maryland, USA

INDIA AmitPatra Indian Institute of Technology Kharagpur, India AshaGarg Aeronautical Development Establishment, Bangalore, India D. Roy Mahapatra Indian Institute of Science Bangalore, India G. V. V. Ravi Kumar Infosys, Bangalore, India Kota Harinarayana Indian Institute of Information Technology Jabalpur, India Nalinaksh S. Vyas

Indian Institute of Technology Kanpur, India P. D. Mangalgiri Indian Institute of Technology Kanpur, India RajagopalanSrinivasan Indian Institute of Technology Gandhinagar, India RanjanGanguli Indian Institute of Science, Bangalore, India R. Umamaheswaran Vikram Sarabhai Space Centre, Thiruvananthapuram, India RamalingamPandiyan ISRO Satellite Centre Bangalore, India S. R.Viswamurthy National Aerospace Laboratories, Bangalore, India Soumendu Jana National Aerospace Laboratories, Bangalore, India Seema Chopra Boeing Research & Technology Bangalore, India SiddharthMukhopadhyay Indian Institute of Technology Kharagpur, India S.Gopalakrishnan Indian Institute of Science, Bangalore, India VanamUpendranath National Aerospace Laboratories, Bangalore, India Vinay Jammu General Electric Global Research Bangalore, India VinayRamanath Seimens Technology Services, Bangalore, India

List of Invited Speakers

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Hybrid algorithms for dynamic system diagnostics

C. Nataraj Abstract As is well known, most dynamic systems in the engineering world are nonlinear and exhibit a bewildering array of phenomena which we, as scientists and engineers, are still uncovering. It is also well known that systems fail due to a variety of complex reasons. Detecting faults in complex engineering systems – or diagnostics – is hence a very a difficult problem and has largely been more of an art than science. In general, diagnostics can be defined as the procedure of mapping the information obtained in the measurement space to the presence and magnitude of faults in the fault space. These measurements, and especially their nonlinear features, have the potential to be exploited to detect changes in dynamics due to the faults. The traditional approach is based solely on actual data and the original baseline of a given system, is valid only for specific systems and conditions, and is not very robust. On the other hand, the mathematical models derived from physics are generally approximate or incomplete. It follows then that these methods are individually not very effective by themselves. Our central thesis is that the diagnostics process can be better achieved with techniques that combine both physics-based techniques and data-based procedures. We have hence been developing a novel hybrid learning system that combines data-based and physics-based approaches. This talk will give an overview of the problem, discuss our novel approach and report on recent successes we have had with both low order nonlinear systems as well as higher order rotor dynamic systems. We will also discuss the outstanding questions to help stimulate further research in this area.

Dr. C. Nataraj is the Mr. and Mrs. Robert F. Moritz, Sr., Endowed Professor in Engineered Systems at Villanova University. Dr. Nataraj received B.Tech from IIT Madras in 1982, and MS in Mechanical Engineering in 1984, and PhD in Engineering Science in 1987 from Arizona State University. He was the founding Director of the Center for Nonlinear Dynamics & Control (CENDAC), an interdisciplinary research center in the College Of Engineering from 2002 to 2007. He resigned from this position to lead the Mechanical Engineering Department as its Chairman from 2007 to 2015.

Nataraj stepped down from the Chairman position in 2015 to start a new interdisciplinary center called Villanova Center for Analytics of Dynamic Systems (VCADS) and serves as its Director. Nataraj has developed and taught over 23 courses and has worked on various research problems in modeling, analysis, control and diagnostics of nonlinear dynamic systems. He has published 220+ papers and book chapters, and is the author of a textbook in vibrations and an upcoming research monograph on nonlinear dynamics. He has received over $10 million in research funding from Office of Naval Research, DARPA, US Navy, National Science Foundation, National Institutes of Health and many companies, and serves on the editorial board of four international journals. Nataraj has been a runner-up for the Lindback award for outstanding teaching and is the winner of the Villanova Outstanding Research Award.

Invited Paper Abstracts and Speakers Biography

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A Smart Platform for Prognostics and Health Management of Rotating Machinery Dinesh Nair Abstract This talk describes the advanced mathematical techniques and algorithms that are used to develop a prognostics and health management (PHM) system for machines that contain rotatory components. We will take a detailed look at the algorithms used at different stages in PHM from feature extraction, health assessment to fault diagnosis and remaining useful life (RUL) prediction. Industrial case studies using a smart PHM platform comprising of intelligent analytics present in the WatchDog Agent™ toolkit for LabVIEW from the Center of Intelligent Maintenance System (IMS Center) and National Instruments’ embedded platform will be presented. We will also discuss how this platform can be used for IVHM to record signals, extract relevant features and accurately model degradation behavior to predict the next failure state and make informed maintenance scheduling decisions.

Dr. Dinesh Nair is a Chief Architect at National Instruments. He currently heads the Embedded and DAQ R&D group at National Instruments, Bangalore. Dr. Nair has been actively involved over the last 20 years in developing innovative and complexalgorithmintensive software with a focus on embedded systems, heterogeneous computing and machine intelligence.He has held multiple roles in the R&D division of National Instruments. Roles involved managing software development groups, technical lead and principal architect, coordinating academic relationships and strategic planning of different product lines. He holds 39 patents. Dr. Nair received his

doctorate degree in electrical and computer engineering from the University of Texas at Austin and is a senior member of the IEEE.

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Combining Model- and Data-Driven Methods for Diagnostics and Prognostics of Complex Systems GautamBiswas Abstract Recently there has been a lot of interest in combining model-based and data-driven approaches to Integrated Vehicle Health Management (IVHM) of complex systems. Model-based approaches to diagnostics and prognostics have the advantage that they are generalizable, and, therefore, the modeling and reasoner techniques developed, apply across many different subsystems and subsystems. However, it is often hard to develop accurate models of complex systems across multiple operating regions, and lack of accuracy and precision affects IVHM performance. With the advances in sensing and computing power in the last decade, large amounts of data are now being collected during operations of complex systems, such as aircraft, automobiles, manufacturing processes, power plants, and power distribution systems. Analyses of these large volumes of data provide opportunities for analyzing complex systems in different modes of operation and under different environmental conditions. More recently, data-driven methods are being applied to anomaly detection, safety analysis, and to help optimize system operations. However, data-driven methods are situation-specific and computationally complex, both in terms of pre-processing and analyzing the data, and are generally not suitable for real-time or near real-time applications. In this talk, I will briefly overview model-based techniques that my group has developed for diagnostics and prognostics of complex, hybrid systems. This involves developing an end-to-end tool chain that supports system modeling, development of diagnostic reasoners, and the development of fault-adaptive control modules to supports to support system operations. We have successfully applied this approach to the diagnostics and prognostics analysis for aircraft and spacecraft systems. In the second half of my talk, I will switch to methods we have developed for data driven diagnostics and anomaly detection for aircraft flight operations. This project, conducted in conjunction with Honeywell

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Research Center, was motivated by a National Aeronautics R & D 2010 document, which outlined one of the fundamental challenges to overcome as “Understanding and predicting system-wide safety concerns of the airspace system …and the vehicles ….., through health monitoring of system-wide functions that are integrated across distributed ground, air, and space systems.” Adopting this framework, we developed a computational architecture that combined data-driven approaches to off-line analysis and learning with online monitoring and reasoning schemes for fault detection, isolation, and prognostic analysis. The focus of the offline learning and analysis was to discover new knowledge that vetted by experts, was introduced into the aircraft system reference model to improve online monitoring and detection. Three case studies showed improved diagnosis of (1) a slow leak in a fuel metering system; (2) early detection turbine blade breaks, and (3) a fuel manifold leak. The additional knowledge derived improved overall detection accuracy, and more importantly, detection 20-30 flights before an adverse event occurred for the aircraft. In a second approach, we developed off-line methods based on unsupervised learning methods to detect anomalous situations. Our analyses found a number of previously undetected errors, related to environmental conditions such as weather, and human errors that resulted in near stall conditions during take-off.

GautamBiswas is a Professor of Computer Science, Computer Engineering, and Engineering Management in the EECS Department and a Senior Research Scientist at the Institute for Software Integrated Systems (ISIS) at Vanderbilt University. He has an undergraduate degree in Electrical Engineering from the Indian Institute of Technology (IIT) in Mumbai, India, and M.S. and Ph.D. degrees in Computer Science from Michigan State University in E. Lansing, MI. Prof. Biswas conducts research in Intelligent Systems with primary interests in hybrid modeling, simulation, and analysis of complex embedded

systems, and their applications to diagnosis, prognosis, and fault-adaptive control. He has also initiated new projects in health management of complex systems, which includes online algorithms for distributed monitoring, diagnosis, and prognosis. More recently, he has combined model-based and data-driven approaches for diagnostic and prognostic reasoning. This work, in conjunction with Honeywell Technical Center has developed sophisticated data mining algorithms to support diagnostics and prognostics. For this work, he received the NASA 2011 Aeronautics Research Mission Directorate Technology and Innovation Group Award for Vehicle Level Reasoning System and Data Mining methods to improve aircraft diagnostic and prognostic systems. His research is supported by funds from NASA, NSF, DARPA, AFRL, and the US Department off Education. His industrial collaborators include Airbus, Honeywell Technical Center, and Boeing Research and Development. He has published extensively, and has over 500 refereed publications. Prof. Biswas is an associate editor of the IEEE Transactions on Systems, Man, and Cybernetics, Prognostics and Health Management, and the IEEE Transactions on Learning Technologies. He is a Fellow of the IEEE, and member of the ACM, AAAI, and the Sigma Xi Research Society.

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Reliability and Availability Modeling in Practice KishorTrivedi Abstract Non-state-space solution methods are often used to solve reliability block diagrams, fault trees and reliability graphs. Relatively efficient solution algorithms are known to handle systems with hundreds of components and have been implemented in many software packages. Nevertheless many practical problems cannot be handled by such algorithms. Bounding algorithms are then used in such cases as was done for a major subsystem of Boeing 787. Non-state-space methods derive their efficiency from the independence assumption that is often violated in practice. State space methods based on Markov chains, stochastic Petri nets, semi-Markov and Markov regenerative processes can be used to capture various kinds of dependencies among system components. However, the resulting state space explosion severely restricts the size of problems that can be solved. Hierarchical and fixed-point iterative methods provide scalable alternatives

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that combine the strengths of state space and non-state-space methods and have been extensively used to solve real-life problems. We will take a journey through these model types via interesting examples.

KishorTrivedi holds the Hudson Chair in the Department of Electrical and Computer Engineering at Duke University, Durham, NC. He has a B.Tech. (EE, 1968) from IIT Mumbai, M.S. (CS, 1972) and PhD (CS, 1974) from the University of Illinois, Urbana-Champaign. He has been on the Duke faculty since 1975. He is the author of a well-known text entitled, Probability and Statistics with Reliability, Queuing and Computer Science Applications, first published by Prentice-Hall; a thoroughly revised second edition (including an Indian edition) of this book has been published by John Wiley. This

book has been recently translated into Chinese. He is a Fellow of the Institute of Electrical and Electronics Engineers. He is a Golden Core Member of IEEE Computer Society. He has published over 600 articles and has supervised 46 Ph.D. dissertations. He is the recipient of IEEE Computer Society Technical Achievement Award for his research on Software Aging and Rejuvenation. His research interests are in reliability, availability, performance, performability and survivability modeling of computer and communication systems. He works closely with industry in carrying our modeling studies, providing short courses and in the development and dissemination of software packages such as SHARPE and SPNP.

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Challenges in Aircraft Health Monitoring Michael H. Azarian Abstract Fault detection and diagnostics of aircraft enable condition-based maintenance for improved operational availability. These applications of PHM present challenges related to the complexity of the systems and the nature of the data that is typically available for analysis. CALCE has been working with aerospace manufacturers and customers in developing improved fault detection and diagnostics capabilities for both rotary and fixed wing aircraft. This presentation will present some of the challenges that have been encountered and solutions used to address them, such as: (i) Extracting useful health indicators from a large number of sensors and condition indicators with varying levels of correlation between them (ii) Identifying anomalies and monitoring aircraft health under the influence of varying and indeterminate operating conditions (iii) Working with absent or inadequate training data (d) Working with data in which maintenance events are not identified (iv) Fusing physics-of-failure models with data driven health monitoring (v) Performing health monitoring of electronics in avionic systems.

Dr. Michael H. Azarian is a research scientist at the Center for Advanced Life Cycle Engineering at the University of Maryland. He holds a Masters and Ph.D. in Materials Science from Carnegie Mellon University, and a Bachelors degree in Chemical Engineering from Princeton University. His research is focused on the analysis, detection, prediction, and prevention of failures in electronic and electromechanical products. He has over 150 publications and 5 US patents on electronic packaging, component reliability, prognostics and health management, and tribology. He has also led several standards committees on reliability for the

IEEE and on counterfeit detection and mitigation for SAE. Prior to joining CALCE he spent over a dozen years in the disk drive and fiber optics industries.

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On Algorithms and Architectures for IVHM: Experiences from Across Domains – Part II AmitPatra (speaker, part II), ArijitGuha, Avik Sadhu, E. P. Nadeer, Jonathan Vasu, Siddhartha Mukhopadhyay(Speaker, Part I) (all co-contributors, in alphabetical order) Abstract This presentation discusses two case studies on the topic. The first study is on automotive engines modeling, fault detection and diagnostics. We first show that some of the conventional modeling techniques like mean value engine models (MVEM) fail to capture the faults in the engine while more detailed models like the within-cycle crank-angle based models are more suitable for this purpose. Then this modeling approach is applied to an automotive engine and algorithms for state estimation, joint state and parameter estimation using various flavours of Kalman filters. Their performances are compared. Finally the above approach is applied to a practical automotive engine and real-time test results are shown to establish the usefulness of the approach. The second case study deals with the modeling, fault detection and health monitoring of Lithium-ion batteries. Firstly the modeling approaches to such batteries are discussed and we choose the electrical circuit equivalent models with fractional order elements as the medium of our work. Parameter and State of Charge (SoC) estimation algorithms are developed using fractional order models. Based on certain battery degradation models, we develop a framework for State of Health (SoH) of a battery and finally create a prognostic framework for the estimation of the Remaining Useful Life (RUL) of a battery. Extensive experimentation are done on lithium ion coin cells and the proposed approach is validated.

AmitPatra received the B.Tech., M.Tech. and Ph.D. degrees from the Indian Institute of Technology, Kharagpur in 1984, 1986 and 1990 respectively. During 1992-93 and in 2000 he visited the Ruhr-University, Bochum, Germany as a Post-Doctoral Fellow of the Alexander von Humboldt Foundation. He joined the Department of Electrical Engineering, Indian Institute of Technology, Kharagpur in 1987 as a faculty member, and is currently a Professor. He was the Professor In-Charge, Advanced VLSI Design Lab, at IIT Kharagpur during 2004-07. Between 2007 and 2013 he served as the Dean

(Alumni Affairs and International Relations) at IIT Kharagpur. His current research interests include power management circuits, mixed-signal VLSI design and process monitoring and diagnostics. He has guided 19 doctoral students and published more than 200 research papers in various Journals and Conferences. He is the co-author of two research monographs entitled General Hybrid Orthogonal Functions and Their Applications in Systems and Control, published by the Springer Verlag in 1996, and Nano-Scale CMOS Analog Circuits – Models and CAD Tools for High-Level Design, published by CRC Press in 2014. He has carried out more than 40 sponsored projects mostly in the areas of power management, process monitoring and fault diagnostics. He has carried out collaborative research projects with ISRO, DRDO, ADE, ADA, General Motors, Tata Motors and General Electric, National Semiconductor Corporation, Infineon Technologies, Freescale Semiconductor and Maxim Corporation. He was a co-investigator in the NPMASS program on IVHM related to internal combustion engines carried out at IIT Kharagpur.

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(TBD) AshaGarg

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Simulation driven learning of fault diagnostics D. Roy Mahapatra (speaker), PadmanabanRaghuraman, NibirChakraborty, NitinBalajee Ravi, RakeshShivamurthy, S. B. Punith, S. R. Hiremath,VeerendraArkachari(All co-authors in alphabetical order) Abstract Simulation driven process flow, design of its architecture, integration of models and data will play crucial role in understanding the influence of IVHM on vehicle platforms. The presentation will summarize the development effort toward these directions and some outcomes. Furthermore, with this backdrop, three different examples of simulation driven process will be illustrated,(1) a probabilistic and accelerated modeling of damages in airframe for scenario based combinatorial simulations and probabilistic life estimation of subsystems (2) sensor-in-loop signal driven real-time detection and monitoring of damages in airframe and the problem of simulation driven optimization of such process and (3) an example of a model based sensor hardware development process for condition based monitoring (CBM). To the end, a statistical machine learning based approach with simulation and sensor data for fault classification in complex structural components will be discussed.

D. Roy Mahapatra is currently an Associate Professor of Aerospace Engineering atthe Indian Institute of Science Bangalore. He obtained his undergraduate degree in Civil engineering in 1998 and a PhD in Aerospace Engineering in 2004 from IISc Bangalore. His research interests are in mechanics of materials, applied mathematical modeling, wave propagation, smart materials for sensors and actuator applications, integrated nano, bio and micro-scale systems dynamics and their applications in health monitoring, diagnostics of materials and

structures.Integrative aspects of materials into structures and their system level functions involving sensing and monitoring have been in the focus of his research in recent times. His earlier research in the areas of computational technique development for solving wave propagation and vibration problems have helped in advancing various different specialized areas such as Structural Health Monitoring (SHM), Noise and Vibration Harnessing (NVH) and Integrated Computational Material Science (ICME). He has published in these areas (over 100 journal articles, one book and several patents) and also worked with industries to solve important problems in these areas.

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Driving Efficiencies through Connected Smart Products G. V. V. Ravi Kumar (Speaker), D. S. Sreedhar Abstract Products are increasingly becoming smarter and connected. This transformation is helping organizations to measure, analyze and optimize the performance of their products continuously. This is leading to comprehensive asset efficiency namely operational, maintenance, information and energy. This talk demonstrates how maintenance and energy efficiencies are realized on a physical assets namely aircraft landing gear system and Chiller through Industrial IOT. DrRavikumar, G.V.V. is Associate Vice President and Head of Advanced Engineering Group (AEG) at Engineering Services, Infosys, with 24 years of research and industrial experience in Aerospace. His areas of interest include Aircraft Structures, Knowledge Based Engineering, Composites, Integrated Vehicle Health Monitoring(IVHM) and Industry 4.0. He is author of more than 40 technical papers in various journals, conferences and other external forums, and a patent to his credit. He has worked on various prestigious engineering design and development programs for aerospace. He is involved in the development of proof of concepts for both structural and system health monitoring in aerospace, as well as in the implementation of diagnostic and prognostics algorithms for engineering applications. Currently invoved in development of Industry 4.0 solutions collaborating with leading universities. He has done his PhD and MTech in Applied

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Mechanics from IIT Delhi, and BE (Hons) from Birla Institute of Technology & Science (BITS) Pilani. Prior to joining Infosys, he worked in Tata Research Design and Development Center (TRDDC), Pune, and Aeronautical Development Agency (ADA), Bangalore. He is member of ASME, ISA and SAE International. He is member of SAE India Aerospace Board. He won many awards including corporate excellence award from American Society of Engineers of Indian Origin.

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Realizing IVHM technologies for the total aircraft Kota Harinarayana Abstract IVHM system aims to deliver an integrated vehicle platform capability such that the platform’s health may be holistically observed. This advances the current state of predictive equipment monitoring ( PEHM ) for which many original equipment manufacturers provide health monitoring of their own particular equipment (eg: engines).The technologies that enabled vehicle level implementation of IVHM are (i) The ability to derive sensor and control system data in a digital format (ii) The ability to locally process , temporarily store and transmit the data to a central point (ii) The ability to capture expert knowledge in analyzing date in order to diagnose and prognose vehicle functional failure using computing processing, and electronically distribute notifications and alerts.The use of diagnostic/prognostic output in flight has been referred to as taking “ direct action “ as opposed to “ differed action “ in which case the results are used to support maintenance, logistics or operational planning. While the onboard and off board systems are essential for achieving end to end IVHM system capability, the implementation of onboard system elements is the greater challenge. The onboard systems are still evolving and they vary with specific vehicle application. Avionics system architecture plays an important role in realizing the potential of IVHM system. In spite of great strides in avionics system architecture, even the latest designs have not supported IVHM system requirements. Implementation of IVHM system on legacy aircraft is a bigger challenge. The talk addresses some of the issues in implementing the IVHM system elements for new and legacy aircraft.

Dr. Kota Harinarayana was born in Berhampur, Orissa, in 1943 and graduated from BHU in Mechanical Engineering, postgraduate in Aero Engineering at IISc, Bangalore. He did his Ph.D. at IIT Bombay and also he is holding a Bachelor’s degree in Law. He started his career in 1967 at HAL. He moved to DRDO HQ in 1970 till 1982and held various positions. He rejoined HAL in 1982 as Chief Designer in NasikDivision. He was deputed to DRDO in 1985 and assumed charge as Director,ADE, Bangalore. He was appointed as LCA Programme

Director in December1985 and he was concurrently holding the post of Director, ADE till June 1986.During 1995 he was elevated as Distinguished Scientist by DRDO. As ProgrammeDirector and Chief Designer of Light Combat Aircraft, he successfully directed theproject leading to flight testing and clearance for limited series production. Thanksto his efforts, India succeeded in developing a state-of-art, high technology fighteraircraft of world class.He is the Fellow of Aeronautical Society of India (former President of theSociety),National academy of sciences and Indian National Academy ofEngineering. He received distinguished alumnus award from Indian Institute ofScience and from IIT Bombay . He was awarded National Aeronautics Prize andFIE Foundation Award in 1996. He received SBI-PragnaPuraskar in 2001. Hereceived the Dr. Y. Nayudamma Memorial Award for 2001. He received theDRDO Technology Leadership Award for 2001. He was honoured with PadmaShri by Government of India in 2002. He received Gujar Mal Modi Sciencefoundation award for the year 2006.Indian National Academy of Engineeringconferred up on him ,the life time contribution award in engineering , for the year2006.He was awarded Shri Om PrakashBhasin award for Science & Technologyfor the year 2007 in the field of engineering including Energy &Aerospace.Hereceived DRDO life time achievement award in 2008.Berhampur universityconferred on him honorary doctorate in the year 2008.

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He was formerly Vice-Chancellor of University of Hyderabad till 15 July, 2005; Chairman,Research council,Centre for wind energytechnology, Chennai; Distinguished Guest Professor, Department of AerospaceEngineering, IIT-Bombay, Indian Technical coordinator for India-Trento/Italy S&T program,Pratt & Whitney Chair professor at Univ of Hyderabad;Dr D SKothari DRDO Chair at ADA,Bangalore.

[WIVHM16-15]

(TBD) Nalinaksh S. Vyas

[WIVHM16-15]

Evolving a National initiative for IVHM in Indian aeronautical sector Prakash D. Mangalgiri Abstract The Indian aeronautical scenario presents a very complex picture for development and implementation of IVHM technologies. In the civil aviation where significant benefits in terms of life-cycle cost reduction and improved safety can be expected from IVHM technologies, almost all of the aircraft are imported and their maintenance is guided (and often “owned”) by the OEMs and international MROs, leaving very little room for implementation of indigenous IVHM technologies. On the other hand, the military aviation, even though comprising largely of aircraft produced in India under License, is poised for significant induction of indigenously developed aircraft such as LCA and its variants. Further, several aircraft are at various stages of development, such as Advanced Medium Range Combat Aircraft (AMCA), Unmanned Combat Aircraft, Unmanned Surveillance Vehicles, Advanced Trainers, Basic Trainers, Medium Range Transport Aircraft (MTA) and Fifth Generation Fighter etc. This offers an opportunity for development of IVHM technologies targeted for implementation in these indigenous programmes. Also, this will help fulfill the growing demand from the Indian Airforce that maintenance cost be reduced, availability be increased and safety of operation be enhanced. While a good amount of foundational work has been done in the country in the area of structural health monitoring, sensor development especially MEMS, and algorithm development such as ANNs, etc, much more work needs to be carried out in several areas relevant to IVHM, such as detection, diagnosis, prognosis, validation of the various algorithms and further in maturing the technologies to a level where they could be deployed in operational vehicles. Considering the high cost of infrastructural resources required for these technology elements and the limited expert human resource scattered over several institutions in the country, it is required that a National initiative be launched to bring together all the expertise in the country to meet the developmental needs of the IVHM implementation in country’s aircraft programmes. The scope of such a National initiative for IVHM needs to focus on addressing the technology elements for a fixed winged aircraft, its inboard systems and propulsion. It should also include small scale demonstrations on lab test set-ups or rigs.Physics-of-Failure (POF) models or data-based models (with built-in statistical analysis) or hybrid models (using both POF and data) need to be built for various equipment and structural components, which then are to be integrated to form sub-system models and then the whole-system models. A framework needs to be looked into for integration and the level of granularity at each level needs to be identified. The major areas of work may be identified as: Sensors and Signal Processing, Data Analysis and Algorithms, Software Architecture and Engineering, Physics-based Analysis and Modelling, Wireless transmission, Testing and Validation Technologies. These developments should be aimed to provide the requisite technology elements to the System labs and the Industry in the country so that their in-house IVHM groups can customize and integrate that work into the aircraft.

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DrPrakash D Mangalgiri graduated from VRCE (now VNIT) Nagpur and then got his M E (Mech) and Ph D (Aerospace) from IISc, Bangalore. He has worked in various organisations, namely, TELCO (now Tata Motors), IISc, NASA Langley Research Center, Aeronautical Development Agency (ADA) and General Motors (R&D), Bangalore. Currently he is Visiting Professor in the Aerospace Department of IIT Kanpur. DrMangalgiri's research interests have developed around composite materials and smart technology. He has worked on the entire spectrum of issues in composites that led to the development of carbon fibre composite technology in India and its incorporation in the

Light Combat Aircraft, Tejas. He has been a key member of the two National Programs for smart materials and MEMs technology in India. At GM-R&D, he explored the use of smart materials in automobiles. His current interests include Structural Integrity, Integrated Vehicle Health Management for Aircraft, and especially, Structural Health Monitoring. DrMangalgiri has served as expert member on various Committees and Boards of Govt of India, such as AR&DB and DST. Recently, he played a key role as the Member-Secretary of the task team set up by AR&DB in formulating a National Program on IVHM. DrMangalgiri has published more than 50 papers in various journals and conferences and has written more than 100 internal technical reports in ADA and GM-R&D. He has edited 3 books and has lectured widely. He is a Fellow of the Aeronautical Society of India and has been President of ISAMPE and ISSS.

[WIVHM16-16] Distributed Hierarchical FDI for System-Level Health Management RajagopalanSrinivasan Abstract Supervision and health management of large scale systems have traditionally relied on single monolithic methods. However, most faults occur at a component level which if allowed to propagate results in system failure, causing downtime and possibly catastrophic outcomes. Monitoring the overall system for detection of such component failures may be futile as deviations in the system-level are often lagging indicators by which time systemintegrity may be compromised. Moreover, modern sensors and components have their own smart diagnostic capability which a supervisory system should make use of. Hence, we propose a hierarchical distributed health managementapproach which uses multiple FDI methods/agents capable of monitoring the system at varying levels of granularity (tag level to system level). When multiple FDI agents are used they need to effectively interact with one another. Hence, a custom ontology is developed to explicitly capture the hierarchy of the system and the nature of the faults. A key issue in monitoring any complex system using multiple independent methods in parallel is that the individual methods may not always concur. A suitable decision fusion/consolidator agent is used which performs the matching between the results from the various FDI agents by mapping their evidences to the ontology and seeking coherence among the various evidences. The proposed approach has been implemented as a multi-agent system using Java WADE distributed environment. The efficacy of this approach is demonstrated on anindustrial-scale case study.

RajagopalanSrinivasan is Professor of Chemical Engineering and Institute Chair at IIT Gandhinagar. Previously, he was with the National University of Singapore and AStar’s Institute of Chemical & Engineering Sciences. Raj received his B.Tech from Indian Institute of Technology Madras and PhD from Purdue University. He was a research associate in Honeywell Technology Center, Minneapolis, before joining NUS. Raj’s research program is targeted towards developing AI-inspired systems

engineering approaches for design and operation of complex systems. He has mentored over 20 graduate students, 15 postdocs & 90 undergraduates. His research has resulted in over 385 peer-reviewed journal and conferences publications. He has delivered plenary & keynote lectures in premier conferences across the

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globe in over 20 countries. He is an Editor of Process Safety and Environmental Protection journal and the Journal of Frugal innovation and on the Editorial board of eight journals. His research has been recognized by best paper awards in several Elsevier and IEEE journals.

[WIVHM16-17] Neural networks, Kalman filters and Fuzzy Logic in Health Monitoring of Aerospace Systems RanjanGanguli Abstract Algorithms play an important role in the diagnostics and prognostics of systems. Typically, sensors placed on the system send signals which need to be processed in order to estimate the system state. A health monitoring system should be able to detect and isolate damage and to suggest prognostic action. Examples of health monitoring systems developed for a helicopter rotor, a jet engine and for wing like plate structures are presented. These systems use neural networks, Kalman filters and fuzzy logic to extract information from sensor data containing uncertainty. Neural networks are used for damage detection in rotor blades. Typical faults such as moisture absorption, loss of trim mass, structural damage, lag damper damage, pitch link damage etc. are modeled using a dissimilar rotor analysis and then neural networks are trained to detect damage. The neural networks are also used to detect cracks in beams and for removing noise from sensor signals. The Kalman filter is used to detect single faults from jet engine data. Data such as engine high and low rotor speeds, fuel flow, exhaust gas temperature, and from pressure and temperature sensors is used to develop a single fault Kalman filter based estimator. Fuzzy logic is used to develop algorithms for detecting damage in beams, composite helicopter rotors and for composite plate structures. A new genetic fuzzy system architecture is proposed for health monitoring. A new sliding window defuzzifier is also developed and is found to be superior to classical defuzzification methods. In addition to measurement noise, the fuzzy system is also found to respond well to measurement noise through a judicious selection of fuzzy membership functions guided by Monte Carlo simulations. In summary, algorithms developed for aerospace system health monitoring are proposed for applications.

Prof. RanjanGanguli is currently the SatishDhawan Chair Professor in the Aerospace Engineering Department of the Indian Institute of Science, Bangalore, India. He received his PhD and MS degrees from the Department of Aerospace Engineering at the University of Maryland, College Park, USA in 1994 and 1991, respectively, and his B.Tech degree from IIT Kharagpur in 1989. He has also worked at GE & Pratt & Whitney. He has published 177 journal papers, 104 conference papers and 5 books. He is a Fellow of American Society of Mechanical Engineers, an Associate Fellow of American Institute of Aeronautics and Astronautics, a Fellow of the Indian National Academy of Engineering, a

Fellow of the Royal Aeronautical Society, UK, and a Fellow of the Aeronautical Society of India. He was also awarded the Humboldt and Fulbright Senior Research Fellowships. His papers have been cited 3960 times in Google scholar and his h-index is 37.

[WIVHM16-18] (TBD) R. Umamaheswaran

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[WIVHM16-19] Spacecraft Mission Planning, Management, Fault Diagnostics Algorithms and Prevailing Procedures RamalingamPandiyan Abstract Not all spacecrafts are in continuous contact with Earth to command and control effectively. It is necessary that a spacecraft has some autonomous features that safeguard the subsystems and prevent malfunctions before it can occur and curtail the spacecraft mission life when there is no direct contact with the spacecraft. The autonomous features are planned based on the past occurrences of failures, careful analyses of the sub-systems behavior in ground check-out testing and further, some heuristic failure cases that are to be avoided in order to extend the mission life at the times of eventuality. The intention of each and every autonomy path is to identify potential autonomous functions, to supply enough information to guide the spacecraft and subsystems to safety and to provide a suitable implementation scheme in order to avert such failures and save the spacecraft. Some few typical ISRO mission scenarios and case studies from missions such as ASTROSAT, Mars Orbiter and Low Earth Orbit (LEO) missions wherein planned on-orbit autonomy has helped to protect and save the missions are presented. The autonomous functions implemented are classified based on the intensity of operations required. If there are events that are having no impact on the mission criticality and which are handled internally at Attitude and Orbit Control System (AOCS) and through TCP, they are classified at the lowest level, say Level A. When the situation demands defined specific attitude orientation for power safety and to avoid communication interruption, Fault Detection, Isolation and Reconfiguration procedures are employed and this class is defined as Level B. Whenever, payload operations are disabled, suspended and aborted, the Level C conditions are enacted, wherein the payloads are switched off for safety and re-orient / maintain the attitude for power safe conditions automatically. Finally, when the spacecraft faces serious safe mode conditions due to sensor failures, rate safe mode limit excess, the spacecraft communication may be interrupted and tumbling etc., and this serious situation is classified as Level-D. The recovery procedures are worked out considering the sub-system limitations, appropriate sequence of commands registration and monitoring and further taken into account the understanding of the linear and nonlinear characteristics of sensors and actuators onboard. The lecture is prepared and planned based on the prevailing procedures at ISRO laid out based on the experience of several dedicated engineers over the years. Though we faced several anomalous situations outside the boundary of the planned FDIR, still it was possible to recover to safety due to the presence of mind at the time of recovery by practicing engineers.

R. Pandiyan is currently the Group Director, Flight Dynamics Group, ISRO Satellite Centre (ISAC), Bangalore, India. He earned his doctoral degree in Mechanical Engineering from Auburn University, Auburn, Alabama, USA in 1994, his Masters degree in Aeronautical Engineering from Indian Institute of Science, Bangalore in 1981 and B. Tech., in Aeronautical Engineering from Madras Institute of Technology, Madras, India in 1979. Earlier to that, he completed his B. Sc., (Mathematics) from A V C College, Mannampandal in the year 1976 with a Silver Medal. He started his career at ISAC and worked as an Engineer during the period 1982-1990 in the then Flight

Dynamics Division. Subsequent to his completion of his doctoral degree, he worked as Assistant Professor in the Department of Aerospace Engineering, Indian Institute of Technology, Kanpur, India during 1994-1995. Later, from 1995 to 1999 he worked as Professor and Head of the Department of Mechanical Engineering at Sri Venkateswara College of Engineering, Pennalur, Chennai, India. He rejoined ISRO again in 1999 and is presently with FDG/ISAC. He has 65 publications in renowned international journals and conferences, which covers the fields Non-linear Dynamics, Structural Control, Spacecraft Flight Dynamics, Spacecraft Attitude Determination, Filtering and Estimation. He is also holding additional responsibility of Deputy Project Director cum Mission Director for the India’s Space Mission ASTROSAT which carries exclusive payloads for astronomical observations in multi-wavelength.

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[WIVHM16-20]

Damage detection & health monitoring of integrated composite aircraft structures S. R. Viswamurthy (Speaker), Nitesh Gupta, M. J. Augustin, AmitabhaDatta, Ramesh Sundaram Abstract Monitoring of aircraft structures can provide vital information regarding the state/health of the structure vis-à-vis operational safety. With increasing usage of composites in airframes and given their brittle nature and complex failure mechanisms compared to conventional alloys, it is becoming increasingly important to evaluate the health of the structure. Presently, primary structures are inspected periodically to detect damages and assess the health of the structure. SHM aims to provide real-time assessment of the state of the structure and can form a part of IVHM technologies with the potential to reduce operating costs of airlines. The key to an effective SHM system for aircraft structures is not only the appropriate sensor selection but also the processing of the sensor data to predict the operating load and the damages, if any, in the structure. Advanced Composites Division of CSIR-NAL, has been at the fore-front of developing SHM technologies for integrated composite aircraft structures for more than a decade. The work has been carried out in all the three aspects of the SHM system namely structural design & analyses, sensor integration and measurement and SHM methodologies and algorithms. The talk/presentation would emphasize on the pattern detection based SHM algorithms which had been developed to tackle the various issues pertaining to disbond detection between skin and stiffener in cocured composite construction at different loads. The effect of sensor failure would also be highlighted in terms of the efficacy of the disbond and load detection algorithms. Impact related events, which may cause internal damages to the composite structures, are also of concerns to the designers. The impact detection system developed at CSIR-NAL, comprising of the sensor measurement hardware and intelligent algorithms to detect the occurrence, location and severity of the impact based on strain sensor data would also be discussed. The talk/presentation will also deliberate about the implementation issues about the deployment of SHM technology from lab to flight level, specifically for aircraft structures.

S.R.Viswamurthy completed his B.Tech in Aerospace Engineering from IIT-Madras in 2001. He then joined Department of Aerospace Engineering, Indian Institute of Science, Bangalore as a graduate student and completed his Ph.D in 2007 in the area of helicopter aeroelasticity. Subsequently, he worked as a Post-doctoral fellow at the Seoul National University for a year in the field of Multi-body formulations for load prediction in helicopter rotor blades and control system components. He joined Advanced Composites Division of CSIR-NAL in December-2008 as Scientist. Presently,

he is a senior scientist at ACD, NAL. His current research interests include: Damage tolerance studies of composite structures, Buckling & Post-buckling behaviour of composite structures, Structural health monitoring, Linear and nonlinear finite element analysis & Digital Image Correlation technique. He is a life member of Indian Society for Advancement of Materials and Processing Engineering (ISAMPE). His research efforts have lead to over 13 international journal articles, 25 conference papers & 10 technical reports. Since 2010, he has also supervised 8 students for Master’s degree thesis/dissertations.

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[WIVHM16-21] Gas Path Health Monitoring of Legacy Aero-Engine: Beginning Soumendu Jana Abstract Aircraft engine is one of the most complex systems demanding efficient monitoring for safe operation and timely maintenance of the aircraft. Modern engine developed in recent times have advance level of anomaly detection with quite a few components with self-health monitoring capabilities. However, legacy aircrafts (and engines) developed in 70s, which did not have any such capabilities, are a major part of Indian Air Force fleet. Such engines are closed to the end of their design life. NAL EHM team in collaboration with other agencies (ADA, IAF, HAL, DRDO and CEMILAC) has initiated development of health monitoring modules for legacy engines. It is understood from literature that Gas Path Analysis (GPA), Vibration and Oil Debris analysis are the often used techniques to assess the health of the engine. In general, the typical parameters recommended for monitoring are temperatures (air inlet, compressor, turbine, bleed air, lube oil, exhaust gas, etc.), pressures (gas path pressures, lube oil, servo control system, etc.), vibrations at various locations, speed, thrust, throttle, oil & fuel flow rate, oil debris, ambient conditions and life usage. EHM module development comprises mainly of data collection, sensor validation, data processing and analysis, diagnostics & prognostic methodologies, etc. Vast database related to engine is required for this purpose. HAL routinely carries out long tests on legacy engines. One such long test provided a good opportunity to initiate EHM activities using test bed data from a single engine. NAL EHM team took active participation in test for data collection purpose to develop EHM modules. Engine was instrumented with additional measurement points required for detailed gas path analysis. Data collected was pre-processed and validated using sensor validation algorithms that were developed. The engine test bed data is being used for development of aero-thermodynamics based engine simulation models and algorithms required for EHM. Evolution of EHM system relies on ‘learning from experience’. Signature related to faults and conditions defining deterioration of components are unique for each type of engine; also within same type of engine, signature varies significantly from engine to engine. Hence, larger the fleet better will be the EHM systems; although at first EHM modules are developed from data of one engine and then tuned to match with data from a fleet of engines.

Soumendu Jana has been working in Propulsion Division, CSIR-NAL since last 18 years after completing PhD degree in Mechanical Engineering from IIT Kharagpur. At present, he is working as the scientist-in-charge of the National Test Facility for Rolling Element Bearings in Propulsion Division of CSIR-NAL. His area of research work is rotor-dynamics, bearings, vibration and external dampers for rotating systems. He has been involved in the area of aero-engine health management activity initiated in CSIR-NAL as a part of IVHM programme, recently. He has published 12 journal

papers, 35 conference papers and 25 project documents during his professional carrier. Worked towards developing a smart rotor support system during postdoctoral research at the School of Engineering, University of Wales, Swansea, UK under a bilateral exchange programme between the Royal Society, London and INSA, India. He has two Indian patents to his credit. Has been member of various learned body such as, Bureau of Indian Standard (Mechanical Vibration & Shock Sectional Committee, MED-28), Tribology Society of India, Aeronautical Society of India, Institute of Smart Structures and Systems (ISSS, IISc, India). Has been awarded National Design Award in Mechanical Engineering for the Year 2013 by National Design Research Forum of the Institution of Engineers, India besides few other awards. Has delivered 11 invited lectures at various technical forums and educational institutes.

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[WIVHM16-22] Airplane Health Management Seema Chopra Abstract This talk will focus on Boeing Airplane Health Management (AHM) capabilities using data analytics. AHM is a decision support tool to make faster and data driven maintenance decisions. This technology can improve repair efficiency and reduce occurrences of unscheduled aircraft maintenance. Unscheduled maintenance occurs due to unexpected incipient or known faults. These faults can cause flight delays and cancellations which increases operational costs to airlines. In this presentation, will also discuss about opportunities, problems and path going forward for next generation AHM technologies.

Seema Chopra is working as Advanced Technologist - Data Analytic in System and Analytics group at Boeing Research and Technology, India. Her current work includes developing next generation advanced health management technologies using real time streaming airline data & big data platforms. Prior to this role, she was with GE as a PHM (Prognostic Health Management) Technical Leader and was involved in design and developing prognostic health management technologies to enable strategic

growth for Condition Based Maintenance for Gas turbines. Seema earned her doctorate degree in Control engineering from IIT Roorkee, India and the focused area was to design Fuzzy Controller with Intelligent Design Approaches with reduced rule set. Seema has 12+ years of research experience in the area of advanced analytics solutions for different applications. Her research includes different areas like Continuous Analytics, Fault Diagnosis and Prognosis, Real time streaming, Big Data platforms, Data Mining, Machine learning, IVHM and Control system. Seema is certified Black belt - DFSS Lean Six Sigma and has 30+ publications in various International/National journals & conferences, 8 Technical reports and 2 filed patent and 2 submitted disclosures and received several awards for leadership and technical expertise including PHM Expertise award from President & CEO, GE Power Gen Services and GE Impact award 2012 from CEO of GE, for volunteering on Mid-Day meal.

[WIVHM16-23] On Algorithms and Architectures for IVHM: Experiences from Across Domains – Part I Siddhartha Mukhopadhyay (speaker Part I), AmitPatra (speaker Part II), AntaraAin, Avik Sadhu, E. P. Nadeer, Jonathan Vasu. PulakHalder, SangeetaNundy, ShrabaniGhosh, SomnathSengupta, Sudipta Mal (all co-contributors, in alphabetical order) Abstract This presentation discusses algorithmic and architectural issues and their interplay in development of Monitoring, Diagnosis and Prognosis Systems for various Engineering Application Domains including those from Automotive, Aerospace, Process and Power Systems. Major issues include: (a) Typical system requirements and architecture (b) Systems modelling including fault modeling (c) Sensing system including sampling rates, choice and location of sensors, signal to noise ratios and their impact on observability, detectability and diagnosability (d) State and parameter estimation algorithms for diagnosis and prognosis (e) Detection/Prediction of faults and impact of SNR on missed detection and false alarms particularly in low SNR situations as in prognosis (f) Fault isolation and diagnosability, reasoning paradigms (g) System implementation, configuration, adaptation and other computational issues (h) Performance assessment, costs and challenges.

Siddhartha Mukhopadhyay received his B.Tech. (Hons.), M.Tech. and Ph.D, all from IIT Kharagpur and joined the Department of Electrical Engineering, I.I.T Kharagpur in 1990. Currently he is a Professor in the Department of Electrical Engg. His current research interests are in CAD and Verification of AMS circuits and systems, Integrated Vehicle

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Health Management, Hybrid Electric Vehicles, Industrial Automation and Cyber-Physical Systems. For more details one may refer tohttp://www.facweb.iitkgp.ernet.in/~smukh/

[WIVHM16-24] Simplified Damage Models and detection Methodologies for SHM of Metallic and Composite Structures S. Gopalakrishnan Abstract SHM is an inverse problem requiring assessing the state of structure using a pre-defined input and measured out-put, wherein the modeling of structures with damages plays a crucial part. This talk will take through some of the novel yet simplified damage models for metallic and composite structures such as delaminations, fiber breaks, notch damages, debonds and porosity. Most models will be based on spectral finite elements. The talk will also focus on some the novel damage detection methodologies that complement well with the various damage models. In particular, some the detection methodologies based on hardware such as laser Doppler Vibrometer will be presented in this talk. The talk will also address the pitfalls and the possible remedies of different modeling tools especially in the context of SHM.

Prof. Gopalakrishnan received his Master’s Degree in Engineering Mechanics from the Indian Institute of Technology, Madras, Chennai, India and Ph.D from the School of Aeronautics and Astronautics at Purdue University, USA. Subsequently, he was a Postdoctoral Fellow in the department of Mechanical Engineering at Georgia Institute of Technology. In the year November 1997, he joined the Department of Aerospace Engineering at Indian Institute of Science Bangalore, where he is currently the Department Chair. His main areas of research interest are Structural Health Monitoring, Wave

Propagation in complex media, and Modeling of NDE systems.Professor Gopalakrishnan has made seminal contributions in the areas of wave propagation in complex media and Structural Health Monitoring (SHM). SHM is a fertile and well-researched area where Prof. Gopalakrishnan has made a mark internationally through his seminal contributions. He is an acknowledged expert in the area of SHM Simulation. His research has enabled solution of many industrially relevant problems thus having direct social impact of the mankind. In addition, he has contributed significantly to the improvement of Science education, especially in the area of SHM in India not only as an administrator of a society where he occupies a primary position, but also through his books and monographs and number of lectures he has delivered in various colleges and universities. Prof. Gopalakrishnan’s contribution towards service to his profession is also immense and outstanding, wherein he has provided leadership in running professional societies, national programs and research boards

[WIVHM16-25] Aerospace IVHM projects at CSIR-NAL: Lessons Learned VanamUpendranath Abstract CSIR-NAL had initiated the Aerospace IVHM activity formally during the year 2011 and conducted a major international Workshop on IVHM and Aviation Safety (www.nal.res.in/wias) during 2012 to draw the national roadmap for Aerospace IVHM. Simultaneously, NAL took up 3 major projects in this domain, viz., Aircraft Electrical System and Wiring integrated Health Management (ESWHM), Landing gear Health Management (LGHM) and Engine Health Management (EHM) as these are the most maintenance intensive systems of an aircraft and also to address the futuristic all electric aircraft scenarios. NAL also coordinated with other institutions namely IISc and Amritha University for related projects in this domain. After an extensive literature survey, field visits to BRDs of IAF, and manufacturing & testing facilities at HAL units across the country from Korwa to Koraput and from Bangalore to Lucknow, networking with various national

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and international organizations (including R&D, Academic, industry, users like IAF, airlines, regulatory etc.,) we took up the IVHM activity at NAL. This exercise enabled us create an ecosystem for the IVHM projects for aerospace in the country by establishing model based system engineering (MBSE)and hardware in loop simulation(HiLS) system platform along with customized test rigs and PoC (comprising functional and fault models and HiLS demonstration) in the case of electrical & wiring system HM, development of physics of failure based functional, fault and integrated models in case of landing gear HM and both data and model driven approaches for Gas Path Analysis with extensive field data in the case of the EHM. In this development framework several algorithms were experimented, results were analyzed and stage is set for the second and exciting stage of the work, namely achieving higher TRLs with a view to seeking certification. However, the journey is not easy in the sense that unless the following issues are taken care the exercise will be futile: (i) Availability of design data, CMMs, QTPs, ATPs, field test reports and flight data of the aircraft LRUs and subsystems under consideration(ii) building customized test rigs for validation (certified test rigs cannot be used for fault injection and analysis because the certification becomes void and will warrant recertification), last but not the least (iii) coordination among various stakeholders. The following are the major observations: (a) To reduce maintenance cost, time and increase availability, IVHM technology is essential (b) IVHM technologies will be specific to the platform though most of the models/methods developed can be reused (c) Data from Designers, OEMs, Test platforms, Maintenance Engineers in addition to CMMs, ATPs and flight test Center are needed to create robust hybrid (data & model driven) approaches (d) Build customised test rigs with industry and R&D organizations joining hands together (e) Networking of R&D, Industry (including OEMs), Academics, Users and involvement of regulatory authorities: Public and Private Partnership model is the key The talk will cover the said aspects in brief with an emphasis on the work done at NAL under the IVHM Mission.

Dr. VanamUpendranath is a Senior Principal Scientist in Structures Division at CSIR-NAL since February 2010. Earlier he worked as a Scientist at CEERI, Pilani for over two and half decades, on embedded electronic systems, industrial networks, Automation in Indian agro and mining industries, mineral and materials technology and wireless sensor networks. DrUpendranath has initiated IVHM activity at CSIR-NAL. He was the coordinator of the NPMASS IVHM projects and a member of the National IVHM Taskforce Cte., setup by the

Aeronautics Board. He has also participated in the SAE IVHM Standards Committee meetings during 2012 at Cleveland, USA and facilitated the happening of HM-1 & E-32 Cte meetings in Bangalore held during 2014 under the aegis of SAE India. Most recently, Upendra was on a sabbatical at the Mechanical EnggDept, Villanova University, USA during June-Sept, 2015 and delivered invited talks and held discussions at NASA Ames, Xerox PARC, FAA and VCADS, Villanova University, USA. He also delivered invited talks at several institutions in the country including HAL Management Academy, Andhra University etc. As the convener, DrUpendranath organized the first NAL-NASA international Workshop on IVHM & Aviation Safety: WIAS (www.nal.res.in/wias), sponsored by IUSSTF, during January 2012. As the Local Chair, he coordinated SAE, India IVHM workshop: (www.saeindia.org/uploads/New%20Vistas/IVHM.html) and participated in the SAE IVHM Standards Cte meetings during Oct. 2014 at Bengaluru. DrUpendranath did his Masters in Electronics from NIT Warangal in 1981 and Ph.D in Microelectronics from University of Trento, Italy in 2005 on Italian Govt. Fellowship, after 20 years of service at CEERI Pilani. During his Ph.D program tenure, he was also a visiting Researcher at EEE Dept, Johns Hopkins University, Baltimore, USA. He was awarded Gold leaf Certificate for the work in top 10% of the research papers presented at IEEE & PRIME, July 2005, EPFL, Switzerland on his PhD work, and received Best R&D project awards at CEERI. DrUpendranath is passionate about the development of indigenous IVHM technology through networking NAL with all those institutions in the country and abroad interested to participate and willing to make the Indian National IVHM Mission a success.

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[WIVHM16-26]

Digital Twin Vinay Jammu Abstract Over the past few decades dramatic improvements in communication and computing technologies have driven the growth of consumer internet which has improved efficiencies, increased customer base and created new business models in many industries including retail, banking, hospitality, and transportation. Industrial internet is going to have a bigger impact on heavy industries such as power, water, airlines, manufacturing, oil and gas, etc. by driving new outcomes and efficiencies in the industries. For example, 1% fuel saving in airline industry today is worth $30B over the next 15 years in the industry. A core capability needed to deliver improved efficiencies in Industrial Internet era is Digital Twin technology. Digital Twins are personalized learning models of different assets that assist in improved decision making related to maintenance and operations of these assets. For example, on the GE90 engine, we have used Digital Twins to increase fleet availability while saving tens of millions of dollars in unnecessary service overhauls. In rail, we are using Digital Twin models of Locomotives to enable our customers to minimize fuel consumption and emissions. The talk will cover core technologies of Digital Twin, including sensing, monitoring, control, prognostics, service and operations optimization to improve efficiencies in multiple industries.

Vinay Jammu is Technology Leader for Asset Performance Analytics and Systems group in Software Sciences and Analytics organization in GE Global Research. He is responsible for 5 different labs located in Bangalore, Schenectady, NY and San Ramon, CA that are focused on driving domain-based analytics to differentiate GE’s Industrial Internet Solutions. Vinay is also a platform leader for Sensing and Lifing Technologies where is drives specific technologies directed towards machine health and condition-based maintenance of GE equipment such as gas turbines, locomotives and Aviation engines.

In addition Vinay serves as focal point for analytics team across GE India Technology Centers in Pune, Mumbai, Hyderabad and Bangalore. Vinay obtained his doctoral degree from University of Massachusetts, Amherst in 1996 in Mechanical Engineering with specialization in the area of fault diagnosis. After a brief stint in Mechanical Technology Inc, NY, he joined GE Global Research, Niskayuna, NY in 1997 as Diagnostics Engineer. After working Global Research, Niskayuna for about 6 years, Vinay relocated to India in 2002 and held multiple roles as Lab Manager and Technology Leader in different organization in GE Global Research. Vinay is a certified master black belt, has 35 patents applications, and 60+ internal and external publications.

[WIVHM16-27] Open challenges in big data analytics for engineering systems VinayRamanath (Speaker), Arun Kumar Kalakanti Abstract Engineering systems such as gas turbines presents a considerable challenge in diagnostics domain, primarily because the nature of data analytics being performed has to be validated with underlying physical laws governing the turbines and the expected pattern of the underlying signals. The talk presents some challenges in this direction and also talks about open challenges in dealing with choice of algorithms, platforms, data variety, velocity volume and veracity. The talk also presents some current challenges and work going in the area of big data integration into computer simulation for the purpose of calibration.

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VinayRamanath has over 15 years of experience in the area of advanced nonlinear multidisciplinary optimization, uncertainty quantification, robust design, design for six sigma, Bayesian approaches for model calibration, advanced visualization and surrogate modeling technologies. Vinay has extensively applied these advanced techniques across gas turbine components and systems for aircraft engines and power generation services. Vinay is a certified Black Belt for Design for Six Sigma and Reliability and is currently working in Siemens corporate research and technology as a Senior Key Expert for Probabilistics.

Panel Remarks – All speakers for the day During these 15 minutes highlight event for the day, all speakers who are presenting on that day are requested to use max 3 pages of slides and discuss about their key observation and understanding. The discussion will be moderated in the form of Q&A by the moderator(s). The objective of this brief discussion is to inform the audience about some of the key aspects that will be further discussed in details by respective speakers. This is to enable the audience interact better during 15 minutes Q&A at the end of each talk. Group Meeting The group meetings of the workshop will be conducted as breakout sessions as indicated in the schedule. The thematic topics are (1) Electrical, electronics, communication and control systems health management (EECCS-HM) (2) Machine, mechanism and interface health management (MMI-HM) (3) Airframe health management (AF-HM) (4) Low resource setting and optimization (LRSO) (5) Vehicle level reasoning, systems software and learning (VLRSSL) The sessions will be chaired and advised by the invited speakers and domain experts participating in the workshop and a coordinator for preparing session proceeding which will be presented on 26

th May afternoon at the group meeting

review. The plan is to discuss and develop concept notes on the following (a) Identify problem areas requiring algorithms (b) Approach and information needed to develop such algorithms (c) Outline example algorithm/flowchart, possible scheme of implementation, identify potential users and platforms (d) Propose new initiatives/collaborations and identify mode of pursuing these efforts Participants are encouraged to meet among themselves and with senior colleagues suitably for networking and discussing collaboration opportunities. IVHM program related discussions will be planned and future efforts needed will be discussed in the Panel discussion on 26

th May.

Workshop Sessions

Networking Events

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Introduction Aircraft health monitoring system as a concept stems from challenges to enhance flight safety. A system that enables automatic detection, diagnosis, prognosis and mitigation of adverse events arising from component failures, is conceptualized in an Integrated Vehicle Health Management (IVHM) system [1]. Electrical parts such as communication and navigation systems, power converters, radar warning receivers, entertainment system etc. need to be continuously monitored to avoid the failure of the system.

IVHM can provide the critical components, sub-system or system,various different diagnostic or prognostic processes, alone or combined. This proactive consideration is the cornerstone of the Prognostics and Health Management (PHM) *2+ philosophy defined as “PHM connects failure mechanisms to system life-cycle management”. Diagnostics The diagnostic process is generally defined as the actions for the detection, localization, and identification of the cause of failure/breakdown. The diagnostic process has two main characteristics: type of methods and steps of the process. The first characteristic is the type of methods.

Quantitative model-based methods

Qualitative model-based methods

Process history based methods Another characteristic is the steps involved in the diagnostic process

Data Acquisition

Data Manipulation

State Detection

Health Assessment Prognostic Prognostic process can be defined as the ability to perform a reliable and sufficiently accurate prediction of the future condition of a system based on its current level of degradation (calculated or from a diagnostic process), projected into the future.The prognostic process has two main characteristics: type of methods and steps of the process. The first characteristic is the type of methods.

Based on experience / statistic

Data driven / based on artificial intelligence

Model based Another characteristic of prognostic is the steps involved in the prognostic process

To Initialize State and Performances

To Project

To Compute RUL (Remaining Useful Life)

Failure in complex electronic systems Following are some of the failure levels in electronic systems.

Level Reason

Chip Level Hot carrier injection; Dielectric breakdown; Electromigration

Component Level Aging degradation; Radiation damage; Intermittencies

Thematic Topic Outline Electrical, Electronics, Communication and Control System Health Management (EECCS-HM)

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Board Level IC, Capacitors; FPGA; CPU; Solder joints; Intermittencies

LRU Level

Communication and navigation; Digital boards; Power/Analogue boards; Connectors

System Level Connectors; Mechanical wear out and corrosion

Current Electronics Health Monitoring Approaches 1. Reliability and Usage based monitoring One of the method for reliability prediction of an electronic components by using Hand Book *3+ “Military Handbook, Reliability Prediction of Electronic Equipment", MIL-HDBK-217.It contains failure rate models for numerous electronic components such as integrated circuits, transistors, diodes, resistors, capacitors, relays, switches, and connectors, etc. MIL-217 requires a greater amount data entered into the model. It also is a little harsher in the calculation of failure rate data. The reliability prediction is generally expressed in FIT [4]. The failure rate of a system usually depends on time, with the rate varying over the life cycle of the system (number of failures for hours) or MTBF (Mean Time between Failures). This guide provides reliability data for RAMS (Reliability, Availability, Maintainability, and Safety). 2. Data-Driven Prognostics Data-driven approach computes remaining useful life (RUL) through statistical and probabilistic method by utilizing historic information and routinely monitored data directly related to the system such as temperatures, pressures, currents, voltages etc. This approach is advantageous over physics of failure approach if the system is complex and subsequently accurate modeling becomes expensive. Moreover, the data-driven approach is applicable to systems, where an understanding of first principles of system operation is not comprehensive. Primary strength of data-driven approaches is their ability to transform high-dimensional noisy data into lower dimensional information for prognostic decisions [5]. The systems do not need prior knowledge which makes it very easy to apply.

3. Physics of failure The physics-of-failure approach is based on first principles of science and technology and provides the insight into not only life and reliability aspects of the component, but also provides details about the various degradation mechanism(s) and thereby improved understand of the associated root cause(s) of the failure. This approach extends Accelerated Life Testing philosophy to investigate the basic failure mechanism. The role of statistics in this methodology is to predict the uncertainty in the estimates of life and reliability.

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Methods of Diagnostic and Prognostic In Case Of Electrical Energy Management Systems Below table shows some of the example of the Diagnostic and Prognostic methods used in a military and aircraft electronic components health management approaches.

References *1+ Suresh Nayagam. “Prognostic Health Management of Aircraft Electronics/Power Electronics”. IVHM Cranfield university.2014. [2] Bastard, Guillaume. "Some Diagnostic and Prognostic Methods for Components Supporting Electrical EnergyManagement in a Military Vehicle." 2nd European Conference of the Prognostics and Health Management Society Nantes, France PHME. 2014. *3+ “http://www.reliabilityeducation.com/intro_mil217.html.” [4] FIDES Group. "FIDES Guide Issue A: reliability methodology for electronic systems."DGA- DM/STTC/CO/477-A,2004. [5] Kabir, Ahsanul, et al. "A review of data-driven prognostics in power electronics." Electronics Technology (ISSE), 35th International Spring Seminar Congress Centre Bad Aussee, Austria.IEEE, 2012.

Components Diagnostic Prognostic

Energy producer components (Rotary Machinery Systems)

Quantitative model based methods based motor current amplitude demodulation

Data driven and Model Based methods are applied

Energy Storage components Quantitative model based on multi-scale Extended Kalman Filter

Data driven methods/methods based on artificial intelligence using learning algorithms.

Energy Consumer Components: (Electronic Controller –Avionic)

Quantitative model based methods for extracting the conditions of use of components from external monitoring

Physics-of-failure (PoF) model based methods that use parameters on the conditions of uses, system life cycle.

Energy Consumer Components: (Electro mechanics–Optronics)

Quantitative model-based methods of condition monitoring can be applied to the mechanical part.

Model based methods for EMA Flight Control Actuators components.

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Introduction IVHM is a subsystem that enables the longer life for a vehicle while keeping a check on maintenance and operational costs with the help of latest technologies involving sensor technology, material processing, damage modeling and system integration. The current practice of scheduled maintenance is costlier which needs to be changed to condition based maintenance aided by IVHM. The purpose of IVHM is to monitor, diagnose and hint the operator or maintenance department regarding the presence of damage. This is done by the help of sensors and data acquisition and later analyzes the data using life predicting algorithms and software systems. Health monitoring systems are employed on both structures and systems. Here we see the various interfaces between components and the vehicle with few examples of how IVHM with algorithms help in monitoring, diagnosis and prognosis of landing gear systems and corrosion damages in structure.

Aircraft landing Gear System Landing Gear system is the most complicated system in an aircraft after the propulsion system. A landing gear is designed with minimum weight, minimum volume for maximum performance and improved life. There are lot of machine mechanism interfaces in a Landing Gear which playvital roles in safe flight and safe landing of an aircraft. These include:

Shock absorption (Force on the landing gear during landing)

Locking mechanism

Extension and Retraction mechanisms

Manual Extension

Anti-skid system

Over temperature burst prevention during braking Development of IVHM systems for monitoring the interface between the components and the vehicle, would give better control over the component and aid in operational cost reduction. Below is an example of landing gear load prediction from flight test data.

Prediction of landing gear loads from flight test data There are different approaches to determine in-service landing gear loads, such as the use of kinematics (accelerations, velocities and displacements) or the use of force measurements (Pressure or strain) [1].We can also use machine learning approach to investigate the predictability of loads induced in the landing gear from the flight parameter recorded. Multi-layer prediction (MLP) models [2] and Gaussian process (GP) regression [3] can be used to model the relation between flight parameters and induced loads, using a database of flight test data

Corrosion Damage Detection Corrosion usually occurs due to extreme weather conditions in aircrafts [4]. Corrosion can occur in different forms such as high temperature corrosion, pitting, exfoliation and metal dust etc. Corrosion is one of the major damages that can occur in an aircraft that if left undetected can lead to the failure of the entire component or structure. So it becomes important to detect the corrosion in its earliest stage and take action towards repair or replacement. Damage detection onboard during flight is impossible without flight intervention. Acellent [4] given in the reference has developed structural health monitoring systems using permanently patched sensors to address these issues. A smart patch diagnostic algorithm and software are used to monitor the damage depth and size. The algorithm is shown below in the fig 1. Calibration is a onetime activity that is conducted in the test facility to

Thematic Topic Outline Machine, Mechanism and Interface Health Management (MMI-HM)

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avail the curves correlating sensor response to damage depth/ severity. For each actuator- sensor path, a damage index is calculated based on the signal lose due the damage. The loss is directly related to damage depth and damage branch out. The index is created for every path for each increment in the crack size thus producing the calibration curve. Environmental calibration is used to account for the environmental changes such as the temperature variations. The depth of any detected corrosion is estimated by using the calibration curves and length is determined by 2D imaging technique [4].

Figure: Algorithm flowchart for detection and sizing of corrosion damage

Reference *1+ Ravi Rajamani, AbhinavSaxena et. Al ‘’Developing IVHM requirements for Aerospace Systems’’. 2013, SAE international [2] E CROSS, P SARTOR et al “Prediction of landing gear loads using machine learning techniques”, 6th

Europeanworkshop: Structural health monitoring *3+ E CROSS, P SARTOR et. AL “Prediction of landing gear loads from flight test data using Gaussian Process Regression”, Proceedings of 9th International workshop: structural health monitoring. Volume 2, pp.1452- 1459, 2013 Stanford University, CA *4+ S BEARD, A KUMAR et. Al. “An SHM system for detecting corrosion damage inaging aircraft”, Proceedings of 9th International workshop: structural health monitoring. Volume 2, pp.1444-1451, 2013 Stanford University, CA

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Introduction The complexity of aircraft operation and maintenance is ever increasing many folds. Other than the periodic maintenance checks, continuous health monitoring systems for the aircraft components are vital for its reliable operations. With the emergence of integrated vehicle health monitoring (IVHM), the vehicle’s self-awareness of health needs to be embedded. The logic in IVHM is to devise a methodology for the subsystems in a structure to interact among them and with some human interface to quantify the damage, or suggest quality improvement. The purpose of the IVHM may differ from identification of critical faults in a system or finding cost reduction opportunity during operation and maintenance or both. The first step in establishing the IVHM framework is to identify the potential systems for monitoring. IVHM can be implemented for the whole system, which is a futuristic target at present, or there can be IVHM developed for individual subsystems. One of the major challenges in developing such systems is how the individual subsystem will interact with each other. Integrated Vehicle health monitoring involves hardware and software framework built in with the operation and maintenance scheduling processes capable of performing diagnosis and prognosis of the component real time while maximizing cost reduction and avoid/warn against unexpected failures. Diagnostic tools will help in identifying the faults whereas prognostic tools will help in identifying futuristic faults by making use of trend information. IVHM makes use of sophisticated mathematical tools and algorithm for fault detection, optimization of resources, triggering of maintenance actions,below shows typical flow in IVHM algorithms. IVHM offers the possibility to maximize/increase available usage of the component/resources by identifying the component capabilities real time. The reliability aspect is improved for a system with IVHM enabled which improves the safety, enhances systems life and reduces ownership cost. IVHM data can beused for making operational decision.

Airframe Health Management Airframe is one of the critical components for the integrity of the aircraft. The airframe is likely to develop cracks and degrade the material strength due to the cyclic loading and corrosive environment. Early detection is necessary to identify these potential damage to prevent the major failure. Present inspection methodologies are laborious and cost intensive. The industry requires real time intelligent load monitoring system, to minimize scheduled inspections and avoid unscheduled inspections while maximizing useful

Thematic Topic Outline Airframe Health Management (AF-HM)

Figure: IVHM in a Nutshell

Identification

Trend Information

Analyze

Data Acquisition

Decide and Act

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vehicle life. There are considerable challenges in establishing IVHM framework for the airframe health management. These challenges are

Lack of adequate trend information

Indication damage from Foreign Object Damage (FOD),

Severity of Fatigue Crack

Corrosion severity on the skin

Anti-icing system design inadequate for conditions encountered.

False alarm

Sensor malfunction Listed below are instances of accidents caused by computer software-related flaws and deficiencies,

Crash due to worldwide bug in barometric altimetry in Ground Proximity Warning System

Crash due to computer failure to warn crew of unset flaps and incorrect thruster indicator

Crash due to digital engine control failure

Break-off of two engines caused by autopilot malfunction

Airplane break-up mid-air when thruster reverser deployed in mid-air; software flaw in proximity switch electronic unit suspected

Accident blamed on experimental software

Accident due to random memory initialization in flight management computers

Crash due to wrong computer readout for navigation Further, the algorithm should identify the secondary effects affecting performance of other subsystem, though the primary cause is emanating from the airframe. The communication between subsystems of IVHM when misinterpreted may fire a false alarm causing unscheduled inspection and therefore ensuring reliability of the framework becomes a challenge. So, IVHM for airframe health monitoring should address all these challenges.

One of the important features of adopting IVHM framework is its economic prospects. When an unscheduled inspection takes place, the airline capacity is lost and there is an indirect cost imposed. The credibility of the company among its customers is strained. IVHM offers diagnostic and prognostic tools which can minimize these incidents, by making the maintenance crew aware of the health status. In this context, IVHM will aid in cost saving. The logistics of replacement components, if needed can also be handled well beforehand, if IVHM triggers the automatic transportation of the component at the required site. Of all this, reliability of operation is the most important parameter, for any airline industry. Reliability can be improved by adopting IVHM framework; thereby the customer satisfaction will also be improved. Potential systems for IVHM should be identified and the framework needs to be tailored to meet the requirement that act as value addition to the system. The main barrier of setting up IVHM is the initial cost of software and hardware development, validation and qualification. The tradeoff is to be made because of the risk included and how it affects revenue is yet to be analyzed. Despite, these initial costs, IVHM proves to have potential in providing huge savings in operation and maintenance. With the advent of cost effective sensor and software technologies, IVHM will enable a paradigm shift to take place in the health management field. Thus, IVHM is the one complete framework that can be implemented in any complex system to maintain the lifecycle in most effective manner in near future. References

[1] J.-B. Ihn, F.-K. Chang and A. H. Speckmann, "Built-In Diagnostics for Montoring Crack Growth in Aircraft Structures," Proceedings of 4th International Conference on Damage Assessment of Structures (DAMAS), Cardiff/Wales, Key Engineering Mat., Vols.204-205, Trans Tech Publications, pp. 299-308, 2001.

[2] G. Bartelds, J. Heida, J. McFeat and C. Boller, "Introduction: Health Monitoring of Aerospace Structures",

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pp. 29-73, IOP Publishing, Staszewski, W., Boller, C., Tomlinson, G., John Wiley & Sons Ltd, 2004,West Sussex, England.

[3] P. Ashok N. Srivastava, P. Robert W. Mah and C. Meyer, "Integrated Vehicle Health Management," Technical Plan,Version 2.03,Aviation Safety Program, Aeronautics Research Mission Directorate, NASA, Washington, D.C., Nov. 2, 2009.

[4] R. Rajamani, A. Saxena, F. Kramer, M. Augustin, J. B. Schroeder, K. Goebel, G. Shao, I. Roychoudhury and W. Lin, "Developing IVHM Requirements for Aerospace Systems," SAE AeroTech Congress and exhibition, Montreal,Canada, 24-26 Sept. 2013 2013.

[5] S. Ofsthun, "Integrated Vehicle Health Management for Aerospace Platforms," IEEE Instrumentation & Measurement Magazine, pp. 21-24, September, 2002.

[6] T. Baines, O. Benedettini, R. Greenough and H. Lightfoot, "State-Of-The-Art in Integrated Vehicle Health Management," Proc. IMechE Vol. 223 Part G: J. Aerospace Engineering, pp. 157-170, 2009.

[7] D. Stephenson, "The Airplane Doctors," Boeing Frontiers 5(1):36---41, 2006. http://www.boeing.com/news/frontiers/archive/2006/august/ts_sf09.pdf

Introduction In aviation industry, the precision of decision making at the appropriate time plays important role in saving costly assets and lives. There have been incidents, where system or the crew failed to adapt to low resources which resulted in catastrophic failures. IVHM scenarios in low resource management settingsmust focus on process optimization in resource-constrained operations. Algorithm development is not just about designing the system and tools so they work when required but also needs to be fail-safe with high redundancy, flexible to adaptation and low-cost. The definition of low resources may stem from many factors such as unavailability of required manpower, limited access to infrastructure facilities, limited availability of equipment supplies, limited access to maintenance, and limited flight data to perceive any fault. The busy airports often are pushed to manage and operate on their limit of available capacity while dealing with heavy traffic and maintaining the service quality. Also, when an aircraft encounters a systemic fault in flight, efficient decisions is what saves the lives of the occupants of the aircraft. Drawing useful inference efficiently from complex systemic fault scenarios would be an important objective of low resource management algorithms. Failure of aircraft systems may have origin from various sources such as atmospheric conditions, internal electrical, mechanical or computational system failure. Following are some of the interesting scenarios (1) Aircraft has failed to deploy landing gear (2) Visibility is severely affected by weather (3) There is no space for the aircraft to land (4) Take-off length is short (5) Foreign object impact causing threat to the systems (6) Time to take-off is very less in a busy airport, yet care must be ensure for not to get into tail vortex of earlier aircraft.

Thematic Topic Outline Low Resource Setting and Optimization (LRSO)

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These are the typical conditions, every aircraft faces. Implications of these factors are often very significant and solving these problems through IVHM algorithms remains open areas of research.Similarly, in the spacecraft IVHM context, satellites are launched into space, where they encounter many unexpected obstacles than what were planned when it was on earth. Use of integrated vehicle health monitoring system development for such events is necessary to add an extra layer of protection. This system is used to identify and eliminate fault at earlier stages. The low resource setting and optimizations algorithms aims at providing the what-if analysis when the resources are minimal and ways to optimize the performance of the systems and also find the hidden faults when the system and to take the relevant decisions. Consider the case, where the Boeing aircraft 747 lands at the wrong airport (headed to Wichita’s McConnell Air Force Base, mistakenly landed at the smaller Jabara Airport). The primary reason for this incident was that Captain’s late decision to abandon the instrumented approach for a visual approach that required him to manually fly the aircraft, as well as inadequate monitoring by the other pilots. The decision making skills of the humans are severely affected when they are subjected to stressful environment. Events such as this, will justify the need to develop the low resource optimization algorithms which helps to identify correct set of decisions based on the available data and improve the reliability and safety of operation. These algorithms can be used in air traffic management systems to make decisions without violating constraints on time and resources to resolve the situation. These algorithms can also be used in maintenance planning to identify resource driven events and plan logistic actions accordingly. The algorithms can be built into aircraft on-board flight system, which aids crew when damaging event is struck. Figure shows the potential damaging events where the low resource setting algorithm can be used to improvise the decision making reliability.

Low resource setting and

optimization

Airport scheduling

Short Runway

Maintenance Scheduling

Logistic Operations

Business level faults

Belly landing due to

landing gear failure

Foreign object Impact damage

Unavailability of Telemetry

Figure: Low Resource Setting and Optimization problems

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Challenges Identification the immature technologies that do not have value to justify neither the

development effort nor the money spend.

Is the low resource setting tools reliable and robust at the time of need?

Procurement cost and added weight due to hardware equipment’s to the aircraft.

How operating conditions affects the performance of the tools.

How well these tools can be integrated into the legacy systems?

State-of-the-art Technologies There are generally two approaches to decision making analysis. The model driven methods simulated the physical model to establish a correlation between experiment and computation values which can be used to solve the resource constrained problems. The other method is data driven, which makes use of statistical representation of flight parameter data collection to drive the low resource management. The machine learning and genetic algorithm are widely used in establishing algorithm for integrated vehicle health monitoring development. The machine learning tools makes use of available data to predict detects and make relevant decision sets to avoid any major hindrance to the flight operation. Genetic algorithms makes use of natural selection process to solve the resource constrained problems.

Summary It is vital for the airline industry to be innovative and look for cost reduction opportunity in every possible way. With the development and integration of low resource setting algorithm, the reliability in automated systems decision making logics will improvise. The low resource management algorithm will play major role, for autonomous system to minimize the damaging events without compromising the mission. References

[1] J. A. Bennell, M. Mesgarpour and C. N. Potts, “Airport Runway Scheduling,”Annals of Operations Research 204(1), pp. 249-270, 2013.

[2] B. Mirkovi, A. Vidosavljevi and V. Tosic, “A tool to support resource allocation at small-to-medium seasonal airports,” Journal of Air Transport Management,vol. 53, issue C, pp 54-64 ,2016.

[3] D. AlAzzawi, HeverMoncayo, MarioG.Perhinschi and AndresPerez, “Comparison of immunity-based schemes for aircraft failure detection,” Engineering ApplicationsofArtificial Intelligence, Vol. 52, pp. 181–193,2016.

[4] K. Worden and G. Manson, “The application of machine learning to Structural Health Monitoring,” Phil. Trans. R.Soc. 365 , pp. 515-537, 2007.

[5] C.Chiu, N.H.Chiu and C. Hsu, “Intelligent aircraft maintenance support system using genetic algorithms and case based reasoning,” International Journal of Advanced Manufacturing Technology 24(5-6), pp. 440-446,2004.

[6] Robert P. Mark,“http://www.ainonline.com/aviation-news/2014-01-06/atlas-identifies-causes-747s-landing-wrong-airport”,2014.

[7] Matt Thurber,“http://www.ainonline.com/aviation-news/blogs/ain-blog-how-wrong-airport-landings-could-happen-and-how-they-can-be-prevented”,2014.

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A Vehicle Level Reasoning System (VLRS) aids in enhancing the safety of Aircraft, such systems comprise of various subsystem units that monitor related components for functional status and relay back operational status to the entities of interest like Avionic System ,Engine ,Hydraulic System ,Fuel System ,Electrical power unit. VLRS is an onboard reasoning system that provides fault detection and isolation capabilities at the aircraft level and estimates remaining useful life of components and subsystems of the aircraft.

Introduction A Vehicle Level Reasoning System (VLRS) aids in enhancing the safety of Aircraft, such systems comprise of various subsystem units that monitor related components for functional status and relay back operational status to the entities of interest like Avionic System, Engine, Hydraulic System, Fuel System, Electrical power unit. VLRS is an onboard reasoning system that provides fault detection and isolation capabilities at the aircraft level and estimates remaining useful life of components and subsystems of the aircraft. The diagnostics and prognostics are vital parts of the integrated vehicle health management technology. Aircraft are very complex in their design and require consistent monitoring of systems to establish the overall vehicle health status. Many diagnostic systems utilize advanced algorithms (e.g. Bayesian belief networks or neural networks) which usually operate at system or sub-system level. The subsystem collects the input from components and sensors to process the data and provide the diagnostic/detection results to the flight advisory unit. Generally, a diagnostic system processes the sensor data and provides results, where the health index and system behaviors are also considered with subsystem generated results. Each sub-system's health index is calculated and passed by the VLRS. Challenges to VLRS VLRS is there to detect and predict faults and failures at the aircraft level. It does this by receiving health information from individual subsystems and fusing them to derive an overall health status for the aircraft. Generally, the reasoning system is an artificial intelligence based software application, hardware device or combination of hardware and software whose computational function is to generate conclusions from available knowledge using logical techniques of deduction, diagnosing and prediction or other forms of reasoning. Presently there is no information exchange connection between the sub-system limitation that leads to the following problems: -

• Non Cross check diagnostic system • Ambiguity in Diagnosis • Limitation on Fault Mitigation

Following are just a few of the issues that may arise when considering an advanced VLRS implementation: • Large Scale integration and commercial-off-the-Shelf: such as ground based maintenance history

databases or archived vehicle usage data will raise issues such as guaranteeing the accuracy and

integrity of the off- board databases.

• Protecting intellectual property

• Flight Crew Interaction

• Interaction with flight Controls

• No deterministic techniques

Functional modules of VLRS The reasoning system is an artificial intelligence based software application, hardware device or combination of hardware and software whose computational function is to generate conclusions from

Thematic Topic Outline Vehicle level reasoning, systems software and learning (VLRSSL)

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available knowledge using logical techniques of deduction, diagnosing and prediction or other forms of reasoning. • Inference Engine This module takes into account health evidence generated from all components, subsystems, and systems within the vehicle aircraft to produce the current diagnostic state or predicts the future evolution of a fault. In this process, it produces a most plausible explanation for all the symptoms provided by various sources creates new hypothesis to track multiple faults and deletes hypothesis that may have weak or no evidence support. • System Reference Model The necessary relationships for the inference process are typically separated as a static system reference model. This partitioning allows the same inference engine software code to be reused on multiple vehicles and minimize certification and qualification costs for deploying VLRS onboard an aircraft. The system reference model, an aircraft loadable software module describes the relationship between evidence generated at the component and/or subsystem level and failure modes that can be mapped to specific maintenance or correction action. • Data mining and learning loop Fleet modeling, data mining and knowledge discovery methods working on historical data can detect anomalies and precursors to critical failure modes. Discovering new patterns and updating old relationships in the system reference model can improve aircraft safety to a higher level continually. Information from this learning loop, resulting in a Change in the reference model enables VLRS to provide accurate health assessment of component, subsystem, or system and support condition based equipment maintenance and replacement. • Communication Interfaces By design, VLRS takes a system wide view of the adverse event detection problem. While the input interfaces define how VLRS receives health information from various member components, the output interface defines how it communicates its outputs to the flight crew (displays), ground maintainer (ground station) or a flight management system for automatic fault accommodation. References *1+ Raj Bharadwaj et al. “Case Studies: Use of Big Data for Condition Monitoring” 9th DSTO International Conference on Health & Usage Monitoring, Melbourne Convention Center, Feb 2015. [2] Khan, F., Jennions, I., and Sreenuch, T., "Integration Issues for Vehicle Level Distributed Diagnostic Reasoners," SAE Technical Paper 2013-01-2294, 2013.

[3] Srivastava, Ashok N., et al. "Vehicle-Level Reasoning Systems: Integrating System- Wide Data to Estimate the Instantaneous Health State." NASA Ames Research Center; Moffett Field, CA United States Jan 2011.

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Invited Speakers AmitPatra Professor, Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India amit[at]ee.iitkgp.ernet.in; amit.patra[at]ieee.org C. Nataraj Professor and Director, Villanova Center for Analytics of Dynamic Systems, Villanova University, 800 E Lancaster Avenue, Villanova, PA 19085, USA Nataraj[at]villanova.edu Dinesh Nair National Instruments, 11500 N MopacExpwy, Austin, TX 78759, USA dinesh.nair[at]ni.com D. Roy Mahapatra Associate Professor, Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560012, India droymahapatra[at]aero.iisc.ernet.in GautamBiswas Professor of Computer Science and Engineering; Dept. of Electrical Engineering and Computer Science; Senior Research Scientist, Institute for Software Integrated Systems; Vanderbilt University, Nashville, 1025 16th Ave South, Nashville, TN 37212 USA gautam.biswas[at]vanderbilt.edu G. V. V. Ravi Kumar Infosys Limited, Electronic City, Hosur Road, Bangalore 560 100, India ravikumar_gvv[at]infosys.com Kai Goebel Deputy Area Lead, Discovery and Systems Health Intelligent Systems Division Coordinator, Prognostics Center of Excellence, Ames Research Center, Mail Stop MS 269-4, Moffett Field, CA 94035, USA K Vijayaraju Sc/Er - 'G', Project Director (NP-MASS) Aeronautical Development Agency, PB No 1718, Vimanapura Post, Bangalore 560 017, India vijay[at]jetmail.ada.gov.in

KishorTrivedi Professor, ECE Dept., Duke University, Durham, NC 27708-0291 USA ktrivedi[at]duke.edu Kota Harinarayana NEB3/401,Sriram Spandana, Challaghatta, Off wind tunnel Road, Bangalore 560037, India hnkota[at]yahoo.com Michael H. Azarian Center for Advanced Life Cycle Engineering (CALCE) 1103 Engineering Lab Building University of Maryland College Park, Maryland 20742 USA Mazarian[at]calce.umd.edu Prakash D. Mangalgiri Visiting Professor, Department of Aerospace Engineering, Indian Institute of Technology Kanpur 208016, India pdmgiri[at]yahoo.com RajagopalanSrinivasan Professor of Chemical Engineering & Institute Chair Indian Institute of Technology Gandhinagar, Vishwakarma Government Engineering College Complex, Chandkheda, Visat-Gandhinagar Highway, Ahmedabad, Gujarat GJ-382424, India raj[at]iitgn.ac.in RanjanGanguli Professor, Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560012, India ganguli[at]aero.iisc.ernet.in; drganguli[at]gmail.com RamalingamPandiyan Group Director, Flight Dynamics Group, ISRO Satellite Centre, Vimanapura, Bangaluru 560017, India pandiyan[at]isac.gov.in S. R. Viswamurthy Advanced Composites Division, National Aerospace Laboratories, Council of Scientific & Industrial Research, HAL Airport Road, Bangalore-560017, India Viswamurthy[at]nal.res.in Soumendu Jana Propulsion Division, National Aerospace Laboratories, Council of Scientific & Industrial Research, HAL Airport Road, Bangalore-560017, India sjana[at]nal.res.in

Complete List of Workshop Participants

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Seema Chopra Systems & Analytics Group Boeing Research & Technology Bangalore, India seema.chopra[at]boeing.com Siddhartha Mukhopadhyay Professor, Department of Electrical Engineering, Indian Institute of Technology Kharagpur Kharagpur 721302, West Bengal, India smukh[at]ee.iitkgp.ernet.in; siddhartha.mukhopadhyay[at]gmail.com S. Gopalakrishnan Professor and Chairman, Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560 012, India krishnan[at]aero.iisc.ernet.in VanamUpendranath Senior Principal Scientist, Structural Technologies Division (STTD), National Aerospace Laboratories (NAL), PO Bag. No. 1779, Kodihalli, Bangalore 560017, Karnataka, India vanam.upendranath[at]gmail.com; vanam[at]nal.res.in Vinay Jammu GE India Technology Center, Plot No. 122, EPIP Phase 2, Whitefield, Bangalore 560066 vinay.jammu[at]ge.com VinayRamanath Siemens technology and services limited, Research in Digitalization and Automation, 84, Hosur Road, Electronics City, Phase 1, Bangalore, India vinay.ramanath[at]siemens.com arunkumar.k[at]siemens.com

Air Frame Health Management (AF-HM) Faculty Participants Sheikh Ghulam Mohammad Associate Professor, Mechanical Engineering, NIT Srinagar. Sgm[at]nitsri.net K I Ramachandran Professor, Centre of Excellence for Computational Engineering and Networking, Amrita School of Engineering, Amrita VishwaVidyapeetham, Coimbatore ki_ram[at]cb.amrita.edu Md. Mursallen Butt

Assistant Professor, Mechanical Engineering, NIT Srinagar Mursaleen[at]nitsri.net Pavan Kumar Kankar Assistant Professor, IIIT Jabalpur pavankankar[at]gmail.com Sukhjeet Sing Assistant Professor, Mechanical Engineering, IIT Ropar. Sukhjeets[at]iitrpr.ac.in Patange Shiva SubbaRao Faculty AcSIR, Scientist STTD, NAL, Bangalore sivapatange[at]gmail.com H Sreedhara Faculty AcSIR, Scientist STTD, NAL, Bangalore hsreedhara[at]nal.res.in

PhD Student Participants P V R SaiKiran Aerospace Engineering, IISc, Bangalorepvrsai[at]gmail.com Shuvajit Mukherjee Aerospace Engineering, IISc Shuvajit[at]aero.iisc.ernet.in KorakSarkar Aerospace Engineering, IISckoraksarkar[at]aero.iisc.ernet.in DuraiArun P. NAL, Bangalore arun.durai.p[at]gmail.com ParagPatil Chemical Engineering, IIT Gandhinagar. Patil.parag[at]iitgn.ac.in RamprasadSrinivasan Jain University Srinivasan.Ramaprasad[at]Honeywell.com Jiwan Kumar Pandit Aerospace Engineering, IISc Jkpandit[at]isac.gov.in

Industry / R&D/ Govt. Participants GopiKandaswamy TCS, Chennai gopi.kandaswamy[at]tcs.com

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VenkataramanaRunkana TCS, Pune venkat.runkana[at]tcs.com ParthaBandyopadhyay ISAC, Bangalore parthab[at]isac.gov.in ShrabaniGhosh ITR, DRDO, Chandipur shrabani.ghosh[at]itr.drdo.in

Electrical, Electronics, Communication and Control System Health Management (EECCS-HM)

Faculty Participants J Valarmathi Professor, School of Electronics Engineering, VIT University, Vellore jvalarmathi[at]vit.ac.in M Manimozhi Associate Professor, School of Electrical Engineering, VIT University, Vellore Mmanimozhi[at]vit.ac.in V P S Naidu Associate Professor, AcSIR, Principal Scientist, NAL, Bangalore Vpsnaidu[at]nal.res.in P MahalakshmiPachaiyappan Associate Professor, School of Electrical Engineering, VIT University, Vellore Pmahalakshmi[at]vit.ac.in C Santhosh Kumar Professor, Machine Intelligence Research Lab, Department of Electronics and Communication Engineering, Amrita VishwaVidyapeetham, Coimbatore cs_kumar[at]cb.amrita.edu K I Ramachandran Professor, Centre of Excellence for Computational Engineering and Networking, Amrita School of Engineering, Amrita VishwaVidyapeetham, Coimbatore ki_ram[at]cb.amrita.edu Sukhjeet Sing Assistant Professor, Mechanical Engineering, IIT Roparsukhjeets[at]iitrpr.ac.in Patange Shiva SubbaRao

Faculty AcSIR, Scientist STTD, NAL, Bangalore sivapatange[at]gmail.com H Sreedhara Faculty AcSIR, Scientist STTD, NAL, Bangalore hsreedhara[at]nal.res.in

PhD Student Participants P V R SaiKiran Aerospace Engineering, IISc, Bangalore pvrsai[at]gmail.com VivekDadu Dr. A P J Abdul Kalam Technical University, Lucknow Vdadu[at]yahoo.com DuraiArun P. NAL, Bangalore arun.durai.p[at]gmail.com Jay Sompura Electrical Engineering, IIT Gandhinagarsompura.jay[at]iitgn.ac.in Vijaylakshmi S. Jigajinni Department of Electrical and Instrumentation Engineering, VTU, Belgaum talk2vijusj[at]gmail.com Gopinath R Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita VishwaVidyapeetham, Coimbatore. rgopinath.gct@gmail.com Ajith Kumar Aerospace Engineering, IIT Kanpur Ajitkr[at]iitk.ac.in SahilSholla Computer Science and Engineering, NIT Srinagar sahilsholla[at]gmail.com Mohammad Irfan Computer Science and Engineering, NIT Srinagar mirfan508[at]yahoo.com ParagPatil Chemical Engineering, IIT GandhinagarPatil.parag[at]iitgn.ac.in RamprasadSrinivasan Jain University Srinivasan.Ramaprasad[at]Honeywell.com Sukhendu Kumar Manna

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Aerospace Engineering, IISc sekhar187[at]gmail.com

Industry / R&D/ Govt. Participants GopiKandaswamy TCS, Chennai gopi.kandaswamy[at]tcs.com PulakHalder RCI, DRDO, Hyderabad pulak.halder[at]rcilab.in ParthaBandyopadhyay ISAC, Bangalore. Parthab[at]isac.gov.in ShrabaniGhosh ITR, DRDO, Chandipur shrabani.ghosh[at]itr.drdo.in

Low Resource Setting and Optimization (LRSO)

PhD Student Participants Shuvajit Mukherjee Aerospace Engineering, IISc Shuvajit[at]aero.iisc.ernet.in KorakSarkar Aerospace Engineering, IISckoraksarkar[at]aero.iisc.ernet.in DuraiArun P. NAL, Bangalore arun.durai.p[at]gmail.com ParagPatil Chemical Engineering, IIT GandhinagarPatil.parag[at]iitgn.ac.in Industry / R&D/ Govt. Participants Srinath Narayan Murthy Research Engineer, Turbo Aero Lab, ATMS, GE Global Research, Bangalore Srinath.Narayanamurthy[at]ge.com ParthaBandyopadhyay ISAC, Bangalore Parthab[at]isac.gov.in

Machine, Mechanism and Interface Health Management (MMI-HM)

Faculty Participants

Sheikh Ghulam Mohammad Associate Professor, Mechanical Engineering, NIT Srinagar.sgm[at]nitsri.net V P S Naidu Associate Professor, AcSIR, Principal Scientist, NAL, Bangalore. Vpsnaidu[at]nal.res.in P MahalakshmiPachaiyappan Associate Professor, School of Electrical Engineering, VIT University, Vellore pmahalakshmi@vit.ac.in C Santhosh Kumar Professor, Machine Intelligence Research Lab, Department of Electronics and Communication Engineering, Amrita VishwaVidyapeetham, Coimbatore cs_kumar@cb.amrita.edu K I Ramachandran Professor, Centre of Excellence for Computational Engineering and Networking, Amrita School of Engineering, Amrita VishwaVidyapeetham, Coimbatore ki_ram@cb.amrita.edu Md. Mursallen Butt Assistant Professor, Mechanical Engineering, NIT Srinagar.mursaleen[at]nitsri.net Pavan Kumar Kankar Assistant Professor, IIIT Jabalpur pavankankar[at]gmail.com Sukhjeet Sing Assistant Professor, Mechanical Engineering, IIT Ropar sukhjeets@iitrpr.ac.in VinayVakharia Assistant Professor, MechEngg.,PanditDeendayal Petroleum Univ., Gandhinagar Vinay.Vakharia[at]sot.pdpu.ac.in H Sreedhara Faculty AcSIR, Scientist STTD, NAL, Bangalore Hsreedhara[at]nal.res.in

PhD Student Participants P V R SaiKiran Aerospace Engineering, IISc, Bangalore. Pvrsai[at]gmail.com DuraiArun P NAL, Bangalore arun.durai.p[at]gmail.com

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Jay Sompura Electrical Engineering, IIT Gandhinagarsompura.jay[at]iitgn.ac.in Gopinath R. Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita VishwaVidyapeetham, Coimbatore rgopinath.gct[at]gmail.com Jay GovindVerma Mechanical Engineering, IIIT Jabalpur royalgovindvrm[at]gmail.com ParagPatil Chemical Engineering, IIT GandhinagarPatil.parag[at]iitgn.ac.in RamprasadSrinivasan Jain University. Srinivasan.Ramaprasad[at]Honeywell.com Jiwan Kumar Pandit Aerospace Engineering, IISc Jkpandit[at]isac.gov.in Sukhendu Kumar Manna Aerospace Engineering, IISc sekhar187[at]gmail.com Industry / R&D/ Govt. Participants Srinath Narayan Murthy Research Engineer, Turbo Aero Lab, ATMS, GE Global Research, Bangalore Srinath.Narayanamurthy[at]ge.com VenkataramanaRunkana TCS, Pune venkat.runkana[at]tcs.com Brijeshkumar Shah Propulsion Division, NAL, Bangalore Shahbrij[at]nal.res.in BalajeeSankar Propulsion Division, NAL, Bangalore balajis_dd[at]nal.res.in

Vehicle Level Reasoning, System Software and Learning (VLRSSL)

Faculty Participants K I Ramachandran Professor, Centre of Excellence for Computational Engineering and Networking, Amrita School of Engineering, Amrita VishwaVidyapeetham, Coimbatore ki_ram[at]cb.amrita.edu Pavan Kumar Kankar Assistant Professor, IIIT Jabalpur pavankankar[at]gmail.com Patange Shiva SubbaRao Faculty AcSIR, Scientist STTD, NAL, Bangalore sivapatange[at]gmail.com PhD Student Participants P V R SaiKiran Aerospace Engineering, IISc, Bangalore pvrsai[at]gmail.com VivekDadu Dr. APJ Abdul Kalam Technical University, Lucknow. vdadu@yahoo.com Shuvajit Mukherjee Aerospace Engineering, IISc Shuvajit[at]aero.iisc.ernet.in DuraiArun P NAL, Bangalore arun.durai.p[at]gmail.com ParagPatil Chemical Engineering, IIT GandhinagarPatil.parag[at]iitgn.ac.in Jay Sompura Electrical Engineering, IIT Gandhinagarsompura.jay[at]iitgn.ac.in Gopinath R. Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita VishwaVidyapeetham, Coimbatore rgopinath.gct[at]gmail.com SahilSholla Computer Science and Engineering, NIT Srinagar sahilsholla[at]gmail.com Mohammad Irfan Computer Science and Engineering, NIT Srinagar mirfan508[at]yahoo.com

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ParagPatil Chemical Engineering, IIT GandhinagarPatil.parag[at]iitgn.ac.in RamprasadSrinivasan Jain University. Srinivasan.Ramaprasad[at]Honeywell.com

Industry / R&D/ Govt. Participants

K N Ramakrishna Southern Electronics Pvt. Ltd. knrkittu[at]yahoo.com Dr. V K Aatre vasudev.k.aatre@gmail.com Dr. A R Upadhya Chairman, TC, AR&DB arupadhaya[at]jetmail.ada.gov.in ar.upadhya[at]gmail.com SamudraDasgupta Seceratary, ADE samudradg[at]ade.drdo.in Dr. Satish Chandra NAL schandra[at]nal.res.in Sham Chetty Director, NAL director[at]nal.res.in Prof. NalinakshVyas IIT Kanpur vyas[at]iitk.ac.in Prof. Dattaguru Jain University datgurb[at]gmail.com CD Balaji Director, ADA cdbalaji[at]jetmail.ada.gov.in Dr S Christopher DG (DRDO) Dr. Satish Kumar DRDO drsatishkumar[at]hqr.drdo.in

Srinath Narayan Murthy Research Engineer, Turbo Aero Lab, ATMS, GE Global Research, Bangalore Srinath.Narayanamurthy[at]ge.com VenkataramanaRunkana TCS, PUNE venkat.runkana[at]tcs.com ParthaBandyopadhyay ISAC, Bangalore Parthab[at]isac.gov.in ShrabaniGhosh ITR, DRDO, Chandipur shrabani.ghosh[at]itr.drdo.in

Special Invitees

Shri MVKV Prasad Director, ADE Shri MZ Siddique Director, GTRE Dr. K Tamilmani DG (Aero) dgaero[at]hqr.drdo.in Dr. Nair drpsnayar[at]gmail.com J Jadhav ADA jadhavj[at]jetmail.ada.gov.in N Viswanadham INSA, Senior Scientist, IISc n.viswanadham[at]gmail.com

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