Abstract - Condition-based maintenance (CBM) is an efficient proactive maintenance strategy based on actual conditions obtained from in-situ, non-invasive tests, and operating measurement. In recent year, prognostics and health management (PHM) has emerged as one of the key enablers for achieving efficient system-level maintenance and for lowering life-cycle costs. This paper overviews methodology of physics-of-failure (PoF) approach and categorizes data-driven approach for the PHM application, summarizes their advantages and disadvantages respectively, and presents a hybrid prognostics approach which incorporate both the advantages of PoF and data-driven approaches for condition-based maintenance.

Keywords - Prognostics, condition-based maintenance,

hybrid, physics-of-failure

I. INTRODUCTION Maintenance is defined as the combination of all technical and associated administrative actions intended to retain a system in a state in which it can perform its required function. Maintenance is traditionally performed as preventive maintenance (PM) or corrective maintenance (CM). PM conducts repair, service, or component exchange at predetermined and fixed intervals of time. CM is performed after a detected breakdown or fault has occurred. They are reactive maintenance strategy. Condition-based maintenance (CBM) is defined as maintenance actions based on actual conditions (objective evidence of need) obtained from in-situ, non-invasive tests, and operating and condition measurement [1]. The objective of CBM is to assess an assets condition during operation and determine when and if maintenance is needed, in order to avoid breakdown or malfunction. Hence, CBM is a kind of proactive maintenance; it can not only improve operational availability but also reduce the life-cycle cost, in contrast with conventional reactive maintenance. CBM is required by the US Department of Defense (DoD) to optimize operational readiness through affordable, integrated, embedded diagnostic and prognostic capability, automatic identification technology, and iterative technology refreshment [2]. It is also US Department of Defense policy that CBM strategies be implemented to improve maintenance agility and responsiveness, increase operational availability, and reduce life-cycle total ownership costs [3].

One of the key enablers of CBM is the development of prognostics and health management (PHM) technology. PHM assesses and quantifies the extent of deviation or degradation from an expected normal operating condition (i.e., health). PHM provides data that can be used to meet several critical goals, including (1) warning of failures in advance; (2) minimizing unscheduled maintenance, extending maintenance cycles, and maintaining effectiveness through timely repair actions; (3) reducing the life-cycle cost of equipment by decreasing inspection costs, downtime, and inventory; and (4) improving qualification and assisting in the design and logistical support of fielded and future systems [4]. In the Study of PHM, the CALCE Prognostics and Health Management Research Center at the University of Maryland first categorized the main approaches: (1) use of expendable devices, such as canaries and fuses that fail earlier than the host product to provide advance warning of failure; (2) modeling physics-of-failure based on life-cycle loads in product utilizing exposure conditions (e.g., usage, temperature, vibration, radiation); (3) modeling data-driven algorithms based on monitored parameter that are precursors for impending failure to reason and predict the failure [4]. In this paper, we introduce a hybrid prognostic model for predicting the remaining useful life (RUL). This model is based on physics-of-failure and date-driven algorithms and utilizes both environmental and usage load measurements (such as temperature and vibration) and in-situ performance / operation parameter measurements (such as current, resistance). To predict the RUL, the two approaches (physics-of-failure and data-driven) were combined using fusion techniques.

II. PHYSICS-OF-FAILURE APPROACH

The physic-of-failure (PoF) approach is founded on the premise that failure result from fundamental mechanical, chemical, electrical, thermal, and radiation processes. The objective of the PoF approach is the PHM is to calculate the cumulative damage due to various failure mechanisms for a product in a given environment and usage condition. The approach to implementing PoF into PHM can be started from a failure mode, mechanism and effect analysis (FMMEA), and also consists of life cycle load monitoring, data reduction and feature extraction, and cumulative damage calculation and remaining useful life estimation, as shown in Fig 1.

A Hybrid Prognostics and Health Management Approach for Condition-Based Maintenance

Huiguo Zhang1, Rui Kang2, Michael Pecht2 1Department of System Engineering, Beihang University, Beijing 100191, China

2Department of Mechanical Engineering, University of Maryland, College Park 20742, USA (jacobzhg@gmail.com)

978-1-4244-4870-8/09/$26.00 2009 IEEE 1165

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Fig 1. PoF based PHM approach

TABLE I

WEAR-OUT FAILURE MECHANISMS FOR ELECTRONICS [0]

Failure Mechanisms Failure Sites Relevant Loads Failure Models

Fatigue Die attach, Wirebond/TAB, Solder leads, Bond pads, Traces, Vias/PTHs, Interfaces T, Tmean, dT/dt, dwell time, H, V

Nonlinear Power Law (Coffin-Manson)

Corrosion Metallization M, V, T Eyring (Howard) Electromigration Metallization T, J Eyring (Black) Conductive Filament Formation Between Metallization M, V Power Law (Rudra) Stress Driven Diffuison Voiding Metal Traces S, T Eyring (Okabayashi)

Time Dependant Dielectric Breakdown Dielectric layers V, T Arrhenius (Fowler-Nordheim)

: Cyclic range : Gradient V: Voltage M: Moisture T: Temperature J: Current density S: Stress H: Humidity

The Process of FMMEA is shown in the area within the dashed line in Fig 1. Failure mechanisms are the physical, chemical, thermodynamic, or other processes that result in failure. Failure mechanisms are categorized as either overstress or wear-out. Overstress failure arises as a result of a single load (stress) condition, which exceeds a fundamental strength property. Wear-out failure arises as a result of cumulative damage due to loads (stresses) applied over an extended time. Within current technology, PHM can only be applied in the wear-out failure mechanisms. Some example wear-outs failure mechanisms for electronics are presented in Table I [5]. PoF based failure models use appropriate stress and damage analysis methods to evaluate the component/system susceptibility to failure for a given geometry, material construction, environmental, and operational conditions [7]. The loading features can be inputs for the failure model to calculate the damage until the item is no longer able to withstand the applied load. Remaining life prediction is the process of estimating the remaining life (e.g., the time in days, distance in miles) through which the product can function reliably, based on the damage accumulation information [8].

Some models used to calculate the damage caused by temperature and vibration loads are summarized in Fig 2. Damage caused by temperature can be calculated in the time domain using Coffin Mansons model [9]. Damage caused by vibration can be calculated in both the time and frequency domains [10-11].

Fig 1. Damage calculation approach for temperature and vibration data

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III. DATA-DRIVEN APPROACH

Data-driven approach allows one to use derived measures of data for estimating the current and future health of the system. In the data-driven approach, a baseline for a system healthy behavior is defined by derived measures and performance parameters distribution based on its performance data of healthy samples. The baseline for a system is used for anomaly detection and degradation identification [12]. Data-driven approach is considered as a black box operation, since it does not include detailed information on material properties. It leverages on the information that can be obtained from primary patterns or relationships exists in data, such as correlation, covariance, residual, and inference patterns between system/component variables, and operating/environmental loads. Data-driven prognostics can be conducted via algorithms modeling and reasoning. In this paper, we categorize the data-driven algorithms for PHM into two groups: machine learning and statistical, as shown in Fig 3. Machine learning is based on acquired data and the translation from raw data to meaningful information is achieved by reasoning, classification and clustering. In general, it includes supervised and unsupervised learning. Statistical techniques are divided into parametric and non-parametric methods based upon whether the information regarding the distribution of the data is known or not.

IV. COMPARISON

The PoF based approach permits the assessment and prediction of system reliability under its actual application conditions. It integrates sensor data with models and takes into consideration failure mechanisms, which enables in-situ identification of the deviation or degradation of a

product from an expected normal operating condition and the prediction of the future state of reliability. It might be the most accurate method for diagnostics and prognostics. However, sufficient information of the product (such as material, geometry, and information), a good understanding of the failure mechanisms, and enough skilled personnel are required to apply the PoF based prognostics method. And the PoF method is not quite suitable for system level analysis. Machine learning is very flexible and can easily adapt to changes. These changes can result from changes in the system itself, in its operating environment, or even in management and its mission statements or expectations. Another advantage of machine learning approaches is that it is suitable for all levels, from component to system. However, the training data, an essential step in machine learning, need to be preprocessed properly, and the validation of the training data is not so easy to perform, which might affect the confidence level of the algorithm. Additionally, because optimization and search methods are often employed in machine learning, their computational complexity and tractability is critical for efficient and effective function. Statistical approach is easier to conduct, due to the fact that technology for the statistical approach has been developed over many years. And it is more economical, for it neither need as much computation as machine learning, nor as many skilled personnel as PoF. However, statistical approaches do not consider the actual usage environments, operation conditions, and failure mechanisms. In additiona large amount of failure data is needed to implement the statistical approach. Comparison on advantages and disadvantages of these approaches for PHM has been summarized in Table II.

Fig 3. Data-driven Approaches for PHM

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TABLE II

COMPARISON APPROACHES FOR PHM

Approaches PoF approach Data-driven approach Machine Learning Statistical Advantages Under actual application conditions

More accurate Flexible Adaptable Suitable for all levels

Easy to conduct Economical

Disadvantages Sufficient product information is required Good understanding of the failure mechanisms is required Skilled personnel is required May not be quite suitable for system level analysis

Training data should be preprocessed Training data are not easy to validate Computational complexity

No consideration of usage environments and operation conditions, Large amount of failure data is needed

V. HYBRID APPROACH FOR PHM

Since all of the approaches have advantages and disadvantages, and there is no single approach that can solve all the PHM problems. In this paper, a hybrid approach based on PoF and data-driven approach is presented to address this problem, as shown in Fig 4. The hybrid approach first involves FMMEA analysis to identify the failure mechanism, the critical components and parameters to be monitored using data driven approach. The continuous health monitoring process provides the information about the systems performance, environmental and operational loading. Then the systems performance is compared with the healthy baseline for anomaly detection. Next step is to isolate the faulty parameter, and using PoF model to access the product damage. By combining the PoF model result with the anomaly trending, then the remaining life of the product can be obtained. When the data-driven approach itself can not define the failure threshold, PoF method can help define it. Normally this can be achieved by tracking the failure mode. In addition, the data-driven method can also calibrate the PoF method to make the prediction more accurate. Normally, the data-driven method will give the better prediction result near the failure point. However for the PoF method, the results will vary due to the uncertainty. Therefore data-driven method can be used to calibrate the PoF result and narrow the uncertainty. When considering the different prognostics levels, the system degradation can be monitored by observing components degradation. A health metrics can be defined as a function of parameters causing the deviation in systems performance. By utilizing parameter isolation, the components causing system degradation are matched with PoF database. The PoF database contains information regarding components, their failure mechanism, and their damage model. The monitored system conditions obtained from the data-driven analysis can then be put into the PoF models for damage estimation. This enables monitoring of fault progression and best estimation of damage accumulation and degradation in system. As an example, if parameter isolation finds high temperature and vibration as

contributing load conditions, PoF models can be used to assess damage in the system. For other performance parameters, statistical features and empirical relationship can also be established. A feature based prognostic approach relies on the ability to track changes in feature and its deviation from normal operating condition. Data-driven techniques are used to extract the features and establish relationships with time. It is especially useful for early detection of failures, where very distinct distribution patterns have been attributed to specific failure.

Fig 4 Hybrid PHM Approach

VI. SUMMARY

CBM with dynamic or on-request intervals based on actual conditions, has attracted much attention recently as a proactive maintenance strategy that is making an impact on both military and commercial maintenance practices. The implementation of CBM can improve maintenance agility and responsiveness, increase operational availability, and reduce life-cycle total ownership costs. Implementation of PHM can address the following three problems in CBM, (1) what is healthy condition of the observed product, (2) if the product is not healthy, where does the degradation or anomaly happen, and (3) when

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the observed product is going to fail or degrade to the point which its performance becomes unacceptable and maintenance action should be taken. Currently research is being conducted to build-up physics-based models and data-driven algorithms to implement the PHM for CBM, both of these two approaches have some disadvantages and no single approach that can solve all the PHM problems. A novel hybrid approach is presented to take advantages of both PoF and data-driven approach. In the hybrid approach, data-driven method is used to calibrate the PoF model. In the meantime the PoF model is used to define failure criteria and thresholds for the data-driven method, and provide an estimate of remaining life based on data-driven results. The incorporation of PoF with data-driven improves prognostic capabilities and accuracy. In the future, due to the increasing amount of product in the world and the competitive drive toward more reliable products, fusion-based hybrid PHM is being looked upon as a cost-effective solution to predict the reliability of electronic products and systems, since it can help identify the most critical failure under products real application condition.

REFERENCES [1] J. Mitchell, Five to Ten Year Vision for CBM (Presented

Conference Paper style), presented at the ATP Fall Meeting, Condition Based Maintenance Workshop, 1998.

[2] US DoD Instruction 5000.2, December 2004. [3] DUSD (LMR) Memorandum, Policy for Department of

Defense Conditioned-Based Maintenance Plus, November 2002.

[4] N.Vichare, and M. Pecht, Prognostics and Health Management of Electronics, IEEE Trans. Components and Packaging Technologies, vol. 29, no. 1, pp. 222-229, March 2006.

[5] M. Pecht, Prognostics and Health Management of Electronics (Book style), NY: Wiley-Interscience, 2008.

[6] S. Ganesan, V. Eveloy, et al., Identification and utilization of failure mechanisms to enhance FMEA and FMECA, in Proc. IEEE Workshop Accelerated Stress Testing & Reliability (ASTR), Austin, TX, 2005.

[7] J. Gu and M. Pecht, Predicting the Reliability of Electronic Products, in Proc 8th Conf. Electronic Packaging Technology (ICEPT), Shanghai, China, pp. 1-8, 2007.

[8] A. Ramakrishnan and M. Pecht, Life Consumption Monitoring Methodology for Electronic Systems, IEEE Trans. Components and Packaging Technologies, vol. 29, no. 3, pp. 625-634, 2003.

[9] K. Cluff, D. Robbins, T. Edwards and D. Barker, Characterizing the Commercial Avionics Thermal Environment for Field Reliability Assessment, Journal of the Institute of Environmental Sciences, vol. 40, no. 4, pp. 22-28, 1997.

[10] J. Gu, D. Barker and M. Pecht, Prognostics Implementation of Electronics under Vibration Loading, Microelectronics Reliability, vol. 47, no. 12, pp. 1849-1856, 2007.

[11] S .Mathew, D. Das M. Osterman, et al., Virtual Remaining Life Assessment of Electronic Hardware Subjected to

Shock and Random Vibration Life Cycle Loads, Journal of the IEST, vol. 50 no. 1, pp.86-97, 2007.

[12] M. Schwabacher, A survey of Data-Driven Prognostics, in Proc. AIAA InfoTech at Aerospace, Arlington, VA, Sept. 2007.

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