overview of sensors project

DNV GL © 2013 SAFER, SMARTER, GREENER1 DNV GL © 2013

Advanced Sensors Projects

Shan Guan Jan 20, 2016

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Outline

Focus in 2015: SHARP and MARV Focus in 2016 and beyond: Sensors Systems Reliability

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Current Focus Areas and Strategies - 2015 Focus Areas: System Health Assessment using Remote Platforms (SHARP): Systematically

integrating sensors with modelling for risk management. Multi-Analytic Risk Visualization (MARV): A risk assessment methodology based on

Bayesian network. Advanced Sensors for Maritime Applications: Prioritizes on using advanced sensors

to manage risk in the fields of Maritime, DNV GL major business area.

-Overall Strategies: Third Party Services: Evaluating and recommending sensors for clients to best fit

their needs. Advisory Service on the latest development of sensor technology including

MEMS/NEMS sensors. Advisory Role for DNV GL business units

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System Health Assessment using Remote Platforms (SHARP)

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Concept of SHARP

SHAPR Application Scenarios “RFID & Motes” Sensors Network for

Remote Monitoring of Refinery Pipeline Corrosion Under Insulation (CUI).

MEMS Wireless Sensors (Accelerometers and Vibration Sensors) Network for Real Time Monitoring of Third Party Damages of Oil and Gas Pipelines.

Wireless Sensors Network for Monitoring Ice Accretion on Rigs.

Sensors Network for monitoring Wind Turbines.

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From MARV to SHARP MARV(Multi-Analytic Risk Visualization): A data driven and highly statistical based

risk assessment tool; Low cost once the model is built; Passive.

SHARP: A tool combines morning and decision making. Higher cost for implementation. Dynamic, prefers real-time data. For SHARP, MARV can serves as the decision making tool.

MARV+ Sensors (Real-time) =SHARP (in a much broader range of application)

For SHARP implementation, major obstacles include:– Application Cases– Sensors Installation and Maintenance (hardware cost, power etc.)– Data Transform and Processing– Decision Making and Action

To demonstrate SHARP through case studies.

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Demo the concept of SHARP Simplified example of Sensor Data Integration with BN models for decision making,

Complex data(including large data) input need to be verified

Sensor Data Transform and Processing

Sensor Data Integration with BN models

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Application Case Study of SHARP: 1

Using wireless sensor network to monitor the displacement of high energy piping at a power station. Potential customer is Power, and O&G industry (Partially funded by Electric Power Research Institute).

Installed a WSN with three sensors (laser), and evaluated multiple sensors including “Draw-wire”, Ultrasonic, Inertial Measurement Unit, Stereo imaging, and laser sensors

TBD: Large/Faster data transfer; Modeling for more accurate risk assessment and failure prediction.

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Optimization of Hull Inspection in FPSO through Monitoring Technologies and Modeling Tools (TL Funded project)

A Summary of 22 types of Sensing Technology for FPSO Hull Monitoring.

Integration of multiple models as the decision making tools.

To demo acoustic emission testing for defects detection (at early stage) for FPSO Hull.

To develop a recommended practice procedure for AET application for Maritime.

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FPSO Frade

A proposal sent to Chevron(Through DNV GLHuston Office) Need a platform to integrate multiple models for decision making in this project

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What is MARV MARV™, Multi-Analytic Risk Visualization, is a risk assessment methodology based

on Bayesian network. Bayesian network uses probabilities to exchange information between models. It

combines Public information, Experts knowledge, Multiple mechanistic models, and Operator’s data in one framework.

Communicating the risk through visualization (MS Surface).

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Roll-Out Marv to the Business Units for Pipeline Risk Assessment

ColumbusTony Alfano

Abu DhabiDelafkaran Maziar

ShanghaiCui Ning

In the last two years, SR&I has been collaborating with business groups for piloting Marv for pipeline risk assessments due to corrosion.4 Projects have been completed in China with three pipeline companies (Project Manager: Shan Guan (piloting in China).2 Projects have been completed in Mid East with two pipeline companies (Project Manager: Gerry Koch (piloting in Mid East).

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Example of Marv Results

• Pilot with CNPC for a 50 km pipeline • Built external corrosion BN model • Assessment the probability of failure due to EC using customer’s data• Compared the modelling results to multiple years of inspections

2011 ILI data

2011 MARV™ prediction

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Sensors Application for Maritime: COMPASS Project

Condition and Operation Monitoring for PerformAnce and Ship Safety

Scope: Towards Class Notation on Condition Monitoring of Ship Machinery

Focuses on the requirements and standards for sensors in the CM service.

External Partner: NRC, R&R Marine Deliverables:

– Report 2015-9197 on “Sensors Requirements for Condition Monitoring and Data-driven Classification: A Case Study”

– Report 2014-9257 on “Sensors for ship machinery monitoring”

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A Case Study: CM of Tunnel Thrusters

Sub-Systems Major Para. to Measure Sensors in CMElectrical

Motor Power/Torque, Vibration Thermography,vibration sensor

Frequency Converter

Temperature, ElectricalMeasurements Thermography)

Propeller Cracks, Vibration Acoustic, Vibration Sensor

Bevel Gear Vibration, Temperature,Cracks

Vibration ,Temperature and Acoustic sensor

Oil Water content/Particles water-in-oil(WIO) sensors

Rolling Bearing Vibration, Corrosion Vibration, corrosion sensor

Transmission Line

Oil pressure, Temperature,

Water content/ParticlesPressure ,Temperature, WIO

sensor

Tunnel Thruster is a simple but representable ship machinery subsystem.

Relatively well known failure modes(~37 failure modes)

Prioritize CM practice based on Failure Modes and Criticality Analysis(FMCA).

Systematically model is necessary for accurately criticality ranking.

Major subsystems of R&R DPN FP thrusters for CM

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Class Note on Condition Monitoring: Sensors and DataDNV GL Minimum Requirement for CM

Data Flow and Sharing Program: Sensor Data Communication(smart sensors are preferable), Wireless Sensor Network, Sensor Data Sharing.

Sensors specifications: four types of CM technologies are mandatory - Vibration Monitoring; Acoustic Emission Monitoring; Wear Debris and Water in Oil Monitoring; and Thermal Monitoring.

Approval Program(including Data quality control and verification): TBD

StandardsSensors IEEE 1451 (smart sensors, plug and play),

NMEA 2000® (Maritime)Sensor network Bluetooth ( IEEE 802.15.1),

Ultra-wideband ( IEEE 802.15.3), ZigBee (IEEE 802.15.4), Wi-Fi ( IEEE 802.11)

Sensor Data Sharing

OPC Classic and OPC Unified Architecture,Open O&M

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Sensor Systems Reliability (SSR) A new project started in 2016, fully funded by SR&I. Selected as a fast track cross-program project (to collaborate with SR&I Maritime) This project will:

- To examine factors that lead to long-term performance deterioration of sensor systems. - To assess the reliability of the sensor components vs total system (power, communications, data processor, and inter-connections). - To investigate using Bayesian networks as a “reliability filter” that processes and assesses sensor outputs prior to passing on to the control system. - To explore predicting the reliably of sensor systems through a combination of modeling and lab testing.

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SSR 2016 Deliverables

Milestones & Deliverables 2016

Deadline

Task 1. A position paper: “Reliability of Sensor Systems, a DNV GL Perspective”.

2016-06-30

Task 2. A report on approach to class rule for sensor

reliability (cross-program fast track project).

2016-06-30

Task 3. Survey of clients on sensor reliability needs.

2016-06-30

Task 4. Identify a case study on predicting the reliability of sensor systems through a combination of

modelling and lab testing.

2016-12-30

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Sensor Systems Reliability (SSR) Sensor System Reliability can be defined as the ability of a sensor system to

perform its required functions under stated conditions for a specified period of time.

A sensor is an object to detect events or changes in its environment, and then provide a corresponding output.

Sensor System comprised not only sensor element, but of components for data logging, data processing and power. It also processes an interface to an outside user for transmitting or displaying informative data.

Schematic of a Sensor System

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Sensor Systems Reliability (SSR) SSR is a critical and challenge issue: -Sensor systems are getting more complex often with multiple sensors (e.g. sensor fusion) - Highly integrated miniature system (e.g. MEMS/NEMS sensors) - The addition of advanced-degree sensor intelligence (e.g. Smarter Systems, Bi-directional response) - Long Term Monitoring Requirements (e.g. Condition Monitoring) - High Consequence (e.g. Leak Detection) - Dynamic Response Requirements (e.g. DP System) - The harsh and extreme working environments(e.g. HTHP, O&G)

In DNV GL’s core business areas (Maritime, O&G, Energy and Health Care) , numerous types of sensors are used for monitoring, inspection and surveying, often in harsh and extreme environments.

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Sensor Systems Reliability (SSR)

Many factors can affect the reliability of a sensors system including:– Sensors Element, – Signal Processing Components, – Power System, – Interface Communicating Components.– Design and manufacture– Installation/Maintenance, including Calibration etc.

Criticality Ranking is useful for prioritization and better management of these factors.

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Sensor Systems Reliability (SSR) SSR can be affected though many ways (i.e. failure modes), however, degradation

is a leading one.

SSR degradation is inevitable even with a solid maintenance plan. Often this will not disable the monitoring system, but is likely to create more problems than it should be solving, i.e. false positives (false alarm), which can cause more damages than without monitoring. For example, a false alarm of water-in-oil sensor can cause dry-dock of the ship with huge financial lost.

Mechanism of sensor system degradation -1.Degradation of the sensing element itself (e.g. aircraft de-icing sensors). -2.Degradation of connectors – usually this is a problem in the sense that degradation of connections results in false positives( e.g. WIO sensor). -3.Degradation of power systems – this is especially important for the sensing system operated through batteries (e.g. corrosion sensors for tank/pipe leak detection in the chemical plant)

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Solution to Sensor System Degradation

High Consequence Scenarios: Faulty sensors can cause high consequence in term of fanatical loss. In this

scenario, the degradation of sensor or connectors is normally a slow process. However, once the degradation exceeds some threshold (i.e. totally ineffective), the consequence could be very high. An example of this scenario is using chemical sensors for pipeline leak detection.

Implementation strategy for preventing this scenario is adding an extra layer of monitoring, i.e. using sensors to monitor sensor.

In practice, one method is to use multiple same types of sensors for validating (through cross checking) the sensor data. The other method is to use correlated multiple different types of sensors for validation.

Some kinds of model validation could be helpful, for example, using principal component analysis to create a sensor validity index for sensor degradation diagnostics.

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Solution to Sensor System Degradation Dynamically Control Scenarios: A Dynamic System using sensors for control purpose. e.g. unmanned aircraft

( gyroscopes for measuring the roll, yaw and pitch angle), or Dynamic Positioning System.

Sensor data can be faulty (drifted, biased or totally off) due to the degradation of sensor system and influence of the working environment.

For this scenario, using sensor to monitor sensor is not a good strategy. A model needs to be placed in the control loop to assess the accuracy(uncertainty)

of sensor data to ensure its quality. For example, a Bayesian network model that consider all factors affecting the reliability of sensor system.

For each application cases, the model needs to be validated through lab testing. Lab testing can determine the threshold of un-reliable sensor data, i.e. the sensor data won’t be trusted when uncertainty predicted by model exceeds the threshold.

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Looking forward We want to develop a metrology to simulate the long term sensor system

reliability for certain applications: – Help DNV GL improve create new business – Help customers to select the best sensors systems (reduce the overall risk)– Help sensors manufactures to improve products quality.

We need:– A modeling tool that can integrate all affecting factors (a platform).– Collaboration from sensor manufacturer (details of products and test results) – Lab testing to validate and improve the model– An application case study for testing (for simulation), either internal or external.

overview of sensors project

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