ASNE Fleet Maintenance & Modernization Symposium 2014 Intelligent Asset Management on Submarines and Ships through Embedded Intelligence Presented by Mr David Lofting MEng MIET OUTLINE • Embedded Intelligence • PHM – Prognos5c Health Management • Benefits of PHM • Case studies v CMAPSS dataset v FMMEA of a power relay WHAT IS EMBEDDED INTELLIGENCE? The ability of a product, process or service to reflect on its own opera5onal performance, usage load, or environment to enhance the product performance and life5me, to increase quality or to ensure customer sa5sfac5on.” “ EMBEDDED INTELLIGENCE • Applications to dictate the technology solution sets • Determine solution spaces for: sensor modalities, communications, power, processors • Physical partitioning of functionality • Packaging for cost, size and compatibility • Assembly methodologies Design for EI Packaging & Interconnect Intelligent Software • Diagnostic and prognostics • Layered ontologies, mapping data • Compatible source-target semantics • Semi-automated linking Applications engineering Manufacturing Solutions • Process consolidation • Cradle-to-cradle: remanufacturing, recycling and re-use • Biomimetics and biomorphism, mechanobiological fabrication System Services • Service and lifecycle requirements to support integration and co-operation of components and services • Optimisation of the services for efficiency across the lifecycle APPLICATIONS OF EMBEDDED INTELLIGENCE Interconnected systems, e.g. wireless agricultural environmental monitoring • Monitors trends for crops using sensors • Can decide if more water/ feed needed • Machine learning influences control ASSET MANAGEMENT • Significant economic investments, e.g. £6.3 billion for HMS Queen Elizabeth • Embedded Intelligence ensures opera5onal performance and life5me of high value assets ASSET MANAGEMENT • CfA -‐ Contrac5ng for Availability, for example Trafalgar class submarines • Reduc5on of Through Life Costs • Contractors benefit from product op5misa5on • Navies benefit from greater availability PHM – PROGNOSTICS AND HEALTH MANAGEMENT • Remaining Useful Life (RUL) predic5ons on high-‐ value assets • Main difference between prognos5cs and condi5on monitoring systems • Also end-‐of-‐life behaviour, premature ‘infant mortality’ system failure iden5fica5ons, etc. • US Dep. of Defense 5000.2 policy document RUL - REMAINING USEFUL LIFE • Data is required to predict an asset’s RUL in real-‐5me • System inputs: historical data, reference standards, sensors, etc. Prognos5cs Health Condi5on Monitoring (PHM) Monitoring system design (CM) system design PROGNOSTIC DIAGNOSIS METHODS • Data-‐driven • Canary • Physics of Failure • Fusion DATA-DRIVEN DIAGNOSIS 1. Extract relevant features of the system 2. Apply sta5s5cal model to determine measure of system health 3. Sta5s5cal predic5on of failure • Do need lots of historical data • Don’t need total in-‐depth understanding of system physics CANARY DIAGNOSIS • ‘Smart coupon’ • Canary has same failure mechanism as system being monitored • Mul5ple canaries with varied ra5o of accelerated canary failure to system failure Cuby Sark PHYSICS OF FAILURE DIAGNOSIS • Primary PHM modelling approaches used are Physics of Failure (PoF) based • Understanding of chemical, mechanical, thermal or electrical cause of system failure • Addresses root causes of failure such as fa5gue, fracture, wear and corrosion FUSION DIAGNOSIS • Uses combina5on of prognos5c methods • Algorithmic techniques could be Dempster-‐ Shafer fusion, Bayesian inference, fuzzy-‐logic inference, neural network fusion, simple weigh5ng/ vo5ng, etc. • Various architectures possible • Adds both to system complexity and confidence levels BENEFITS OF PROGNOSTIC SYSTEMS § Enables auxiliary systems to take over before the primary system suffers catastrophic failure § Increased understanding and insight of systems being monitored can highlight development opportuni5es § Helps to an5cipate maintenance requirements, therefore reducing costs and aiding logis5cs CASE STUDY – CMAPSS DATASET • Prognos5c applica5on where extensive datasets are available • Ideal but unusual in Naval plaeorm applica5ons • Novel approach combining v Autoregressive dynamic modelling tool v State-‐of-‐the-‐art classifier • Remaining Useful Life (RUL) predic5ons WHAT IS NASA’S CMAPSS? • Commercial Modular Aero-‐Propulsion System Simula5on Turbofan engine • Simulates 90K lbs thrust turbofan engines with variable characteris5cs • Modelled mostly with MATLAB and Simulink • Advanced deteriora5on and fault simula5on DATASET • Dataset of 100 turbofan engines • Each engine had samples from 21 sensors, and 3 opera5ng condi5ons associated • 80% engines used for algorithm training and 20% for verifica5on • Used to train both algorithm modules ALGORITHM • Linear Weighted Projec5on Regression (LWPR): v Dynamic real-‐5me learning modelling tool for func5on approxima5on v Predicts future states of the asset • Random Under-‐Sampling Boos5ng (RUSBoost): v State-‐of-‐the-‐art classifier v Discriminates between healthy and faulty opera5on given a current state of the asset COMPARISON OF ALGORITHM TO GROUND TRUTH Algorithm predicts RUL with mean square error of 6 engine opera5on cycles Test results for 20 engines CMAPSS CASE STUDY SUMMARY • Representa5ve dataset and turbofan simula5on tool provided by NASA • Novel 2-‐stage algorithm approach for RUL • RUL algorithm performance verified for manufacturing uncertainty and mul5ple faults • Ideal scenario where lots of data available CASE STUDY – FMMEA OF POWER RELAY • Electromechanical relay with no historical data available – much more common for naval plaeorms • Failure Modes, Mechanisms and Effects Analysis (FMMEA) • Endurance tes5ng • Iden5fica5on of failure precursors TEST SET-UP Relay was contained in an isola5on chamber, so test results could not be contaminated by par5cles. FMMEA • Failure Modes, Mechanisms and Effects Analysis • Physics of Failure based approach • Can document as table with headings of Failure Mode, Failure Mechanism, Failure Effect, Likelihood, Cri5cality, Repairability FMMEA methodology DESIGN OF EXPERIMENT • Mul5ple relays tested for endurance in controlled environment for different voltage source levels • Sensing parameters monitored: voltage, current, case temperature and external temperature • Failure criteria set RELAY ANALYSIS • Relays disassembled • Contacts subjected to: v Topological analysis using a Zygo white light interferometer v Energy Dispersive X-‐Ray Spectroscopy • Data processed in MATLAB to extract features FAILURE MODE INVESTIGATION Energy dispersive X-‐ray spectroscopy analysis Topological analysis of contacts with white light interferometry FAILURE PRECURSORS Pip and crater features -‐ long arc dura5ons, shown by spikes in opening 5me & mean open current Erosion failure -‐ spikes in resistance, which result in an increased closing 5me Unacceptable opening 5me -‐ steady upward trend in open 5me PROGNOSTIC MONITORING Raw opening 5mes, 30V test • Current and voltage monitoring can detect failure precursors – no lab equipment needed • Prognos5cs can be implemented in situ with Embedded Intelligence systems APPLICATION OF RELAY DATA FOR PROGNOSTICS • Inexpensive to implement • Safety and reliability • Maintenance • Logis5cs Example monitoring system CONCLUSION • Prognos5c systems offer many advantages v Asset/ component Availability v Embedded Intelligence func5onali5es v Development • Case studies demonstrate: v Verifiable RUL using data-‐driven algorithm v Extrac5ng failure precursors from PoF approach without pre-‐exis5ng dataset
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