Reliability, Availability and Maintenance (RAM) & Prognostics and Health Managemenrt (PHM) - 4.0

Transcription

1 Reliability, Availability and Maintenance (RAM) & Prognostics and Health Managemenrt (PHM) Enrico Zio Chair on Systems Science and the Energy Challenge CentraleSupelec, Fondation Electricité de France (EDF), France Energy Department, Politecnico di Milano, Italy Aramis Srl, Italy

2 INDUSTRY 4.0 2

3 Industry

4 SMART 4

5 Smart grids, Smart Cities and Eco-Industrial Parks Application of Internet of Things concept in Smart Cities to tackle urban challenges pollution, energy efficiency, security, parking traffic, transportations etc. Internet of Things in Smart Grids Eco-Industrial Parks Source: IOT Phillippines INC. Source: Kalundborg Symbiosis 5 5

6 Industry 4.0- (Cyber-Physical/Smart) Systems 6

7 There are now TWO railway systems 7 Computer systems Bits and bytes Interfaces with the real world But behaves differently Quick fix and rapid change Answers to programmers coding laws and practices Physical train systems Living passengers and freight Wear and tear Long term investments Answers to laws of physics

8 And it s not just Great Britain 8 #DIGITALSNCF BAHN

9 The Internet of Trains From reactive to predictive maintenance Increased up-time through significant reduction of un-planned downtime. Extension/flexibility of maintenance intervals because we understand the risk. Reduced labour costs: quicker root-cause analysis, improved firsttime-fix rate, etc. Thameslink: Performance-based maintenance contract requiring nearlyrun-time analysis of diagnosis and process data. Metro Riad: availability targets (40 seconds arrival- departure per train) can only be reached with data-enabled services. 9

10 RAM & PHM

11 11 Big Data 2.8 Trillion GD (ZD) generated in 2012

12 Are you a believer? 12

13 The Big KID 13

14 Big Knowledge(ID) 14

15 Big (K)Information(D) 15

16 16 Big (KI)Data

17 Can the Big KID become SMART for Reliability Engineering? 17 Application

18 FAILURES 18

19 19 Failures Failures Prevented by Design for Reliability Maintenance Normal Degraded Failure Time

20 20 Problem statement Failures Prevented by Design for Reliability Maintenance Normal Degraded Failure Time

21 Reliability and Availability Engineering

22 22 Failure modelling (binary) Failure ON OFF As Good As New Failed X(t) 100% 0% t System unavailability U(t) = Pr[X (t) <100%] U(t) = Pr[X (t) =0%]

23 Degradation-to-failure modeling Multi-state: Failure Failure Mode 1 Degradation state 1 Degradation state n 1 ON OFF Failure Mode M Degradation state 1 Degradation state n M X(t) 100% 75% 50% 25% 0% t Demand of system performance System unavailability D(t) U(D,t) = Pr[X (t) < D(t)] 23

24 Reliability SMART Reliability Engineering: Big KID opportunities Reliability? KID (Knowledge, Information, Data) Model Year Highly reliable Sufficient failure data Physics knowledge Expert judgment Field data Statistical models of time to failure Stochastic process models Physics-based models Multi-state models

25 SMART Reliability Engineering Multi-State Physic-Based Models Alloy 82/182 dissimilar metal weld of piping in a PWR primary coolant system Physical laws Multi-state physics model of crack development in Alloy 82/182 dissimilar metal weld

26 SMART Reliability Engineering: Big KID opportunities Degradation processes Internal leak Initial state Failure state λ 32 λ 21 λ Piecewise-deterministic Markov process (PDMP)

27 SMART Reliability Engineering: Big KID opportunities MC Simulation Finite-volume scheme

28 28 Maintenance engineering 4.0 Failures Prevented by Design for Reliability Maintenance Normal Degraded Failure Time

29 Maintenance and PHM Engineering 4.0

30 Maintenance 4.0: How? Integrated maintenance process, supported and informed by knowledge of components/systems/process behaviors through high-quality data, real-time information effective models and methods to process the information effective organizational processes to implement the solutions KID + Intelligence Prognostics and Health Mamagement (PHM) for Predictive Maintenance (PrM)

31 Maintenance Maintenance Corrective Maintenance Planned Periodic Maintenance Condition Based Maintenance (CBM) Predictive Maintenance (PrM) Prognostics and Health Management (PHM) PHM is fostered by advancements in: 31 Sensor Algorithm Computation power

32 PHM for what? PHM in support to CBM and PrM Fault Normal Conditions Vibration Detection Abnormal Conditions Equipment Temperature t t Fault Diagnostics Anomaly of Type 1 Anomaly of Type 2 Anomaly of Type 3 Decision Maker No Maintenance Maintenance Maintenance Decision Sensors measurements Fault Prognostics Remaining Useful Life (RUL) 32

33 PHM: why? (Industry) 33 Increase maintainability, availability, safety, operating performance and productivity Reduce downtime, number and severity of failure and life-time cost

34 PHM: why? (Business) 34 Improve cash flow, profit stream and utilization of assets Guarantee long term business Increase market share

35 35 PHM: how? (Fault detection) Signal Real reconstructions measurements MODEL OF PLANT BEHAVIOR IN NORMAL OPERATION Nominal Range-based Physics-based Data-Driven (AAKR, PCA, RNN, ) Abnormal Condition

36 Norm Node 14 Wavelet PHM: how? (Fault diagnostics) Signal measurements representative of the fault classes: «x 1,x 2, x n, class» Norm Node 5 Wavelet Peak Value Empirical classification methods: Support Vector Machines K-Nearest Neighbours Multilayer Perceptron Neural Networks Supervised clustering algorithms Ensemble of classifiers x 1 x 2 x 3 Empirical Classifier C 1 = Inner race C 2 = Balls C 3 = Outer race 36

37 37 PHM: how? (Fault prognostics) Rotating machinery (e.g. pump) t Health Index FAILURE THRESHOLD t Prognostic model Health index prediction t p t f t t p t RUL ˆ Baraldi, P., Cadini, F., Mangili, F., Zio, E. Model-based and data-driven prognostics under different available information (2013) Probabilistic Engineering Mechanics, 32, pp E. Zio, F. Di Maio, A Data-Driven Fuzzy Approach for Predicting the Remaining Useful Life in Dynamic Failure Scenarios of a Nuclear Power Plant, Reliability Engineering and System Safety, RESS, /j.ress , F. Di Maio, K.L. Tsui, E. Zio, Combining Relevance Vector Machines and Exponential Regression for Bearing RUL estimation, Mechanical Systems and Signal Processing, Mechanical Systems and Signal Processing, 31, , 2012.

38 Uncertainty management (prognostics) Sources of uncertainty: 1) noise on the observations (measurements) 2) intrinsic stochasticity of the degradation process 3) unknown future external/operational conditions 4) Modeling errors, i.e. inaccuracy of the prognostic model used to perform the prediction Uncertainty on the RUL prediction? 6 x RUL pdf estimate True RUL Maximum acceptable failure probability is 5% RUL Prognostic Model Present Time Probability to have a failure in this interval is lower than 5% time for maintenance 38

39 RAM & PHM 4.0 Nominal Functioning Threshold Failure Threshold t d t f System failure behavior: -Component intrinsic reliability -Component Maintanibility Remaining Useful Life (RUL) PHM system: -Diagnostic Performance -Prognostic Performance PHM metrics Predictive maintenance policy criteria: -Inspections interval -Time required to prepare maintenance based on predicted RUL MODEL System Availability (Economic Indicator) Probability that the component fulfills the assigned mission at any specific moment of the lifetime System Reliability (Safety Indicator) Survival probability L. Bellani Reliability and Availability with PHM POLITECNICO DI MILANO

40 Conclusions 40

41 (Complex) Systems 41 Series R( t) N i 1 Ri( t) Parallel N R( t) 1 1 Ri( t) i 1 Standby

42 42

43 Complexity and reliability complexity, reliability time 43

44 Complexity and vulnerability/risk: surprises 44 Complexity? Surprises

45 Conclusions: Big KID and Smart KID FTA FMECA Hazop Bayesian Belief Networks Process and Stochastic Flowgraphs ETA Fuzzy Logic Neural Networks Monte Carlo Simulation Petri Nets Systems Optimization Algorithms Clustering Algorithms Graph Theory Complex Network Theory 45

46 Conclusions: Smart KID for Reliability Engineering 46 SMART KID Knowledge Information Data Simulation, Modeling, Analysis, Research for Treasuring Knowledge, Information and Data (for Reliability Engineering)

47 Conclusions: Smart KID for Reliability Engineering 47 E. Zio, IEEE Trans on Reliability, 2016 Some challenges and opportunities in reliability engineering

48 Thanks 48 for your attention