Condition Monitoring, Fault Diagnostics and Prognostics of Industrial Equipment Enrico Zio
PROGNOSTICS AND HEALTH MANAGEMENT (PHM)
PHM: what PHM = DDP = = + + Detect Diagnose Predict Normal operation f 1 f 2 Remaining Useful Life
Condition Monitoring and Prognostics and Health Management 4 Condition Monitoring Detection Diagnostics Prognostics Maintenance
Ideal Maintenance T m =T f dt Component s life fully exploited Unavailability and further failures due to maintenance actions are avoided T m = maintenance time T f = failure time
Uncertainty and Maintenance The failure mechanisms have uncertainty associated with their occurrence in time: Inherent physical randomness of the degradation and failure processes Model used to assess the performance of the system imprecise reproduction of reality Early Late prediction prediction Failure When to perform maintenance: non-trivial decision
Diagnostics: fault detection and classification Prognostics: lifetime estimation f 1 Diagnostic system Normal operation f 2 FAULT DETECTION early recognition Forcing functions Measured signals f 1 Prognostic system Lifetime estimation P Prognosis FAULT CLASSIFICATION correct assignment f 2
Maintenance Intervention Approaches Maintenance Intervention PHM Unplanned Planned Corrective Replacement or repair of failed units Scheduled Perform inspections, and possibly repairs, following a predefined schedule Conditionbased Monitor the health of the system and then decide on repair actions based on the degradation level assessed Predictive Predict the Remaining Useful Life (RUL) of the system and then decide on repair actions based on the predicted RUL
METHODS 9
Desiderata from prognostics methods (1) 1. Accurate prediction Early prediction Late prediction Fault detection Predicted Failure Failure Predicted Failure RUL 2. Short time needed for prediction Fault detection Failure prediction Failure Time needed for prediction << RUL Time available for corrective action
Desiderata from prognostics methods (2) 3. Robustness INPUT INFORMATION Accurate RUL estimate IMPRECISE INCOMPLETE 0 500 1000 PROGNOSIS HI 0 500 100 time 0 500 100 4. Confidence estimation RUL INPUT INFORMATION HI IMPRECISE INCOMPLETE 0 500 1000 PROGNOSIS 0 500 100 time t d t f 0 500 100 confidence interval
Techniques (I) 12 Methodology box Component Data & Information Component Health Assessment Fault Detection Diagnostics Prognostics
Techniques (II) Data processing/ feature extraction Feature selection Fault detection Fault Diagnosis Failure prognosis Principal Component Analysis Filter Approaches (Genetic Algorithm/Differential evolution + correlation analysis) Principal Component Analysis K Nearest Neighbors Kalman Filtering Wavelet Analysis Wrapper approaches (Genetic Algorithm/Differential evolution + classification algorithm) Autoassociative Kernel Regression Supervised clustering methods Particle Filtering Spectral Analysis Random Feature Subset Evolving Clustering Classification Tree Neural Networks Selection Method Statistical indicators (RMSE,kurtosis, skewness, ) Sensitivity Analysis Techniques Neural Networks Fuzzy Similarity Fuzzy similarity Box Plot Analysis Ensemble systems Artificial Neural networks Ensemble systems Correlation Analysis Fuzzy Similarity Bagged Ensemble Support Vector Machines Spectral Analysis Fuzzy C-Means Adaboost Relevance Vector machines Sequential Probability Ratio Test Learn ++ Fuzzy C-Means
Approaches Wide range of data & information Wide range of different approaches DATA-DRIVEN MODEL-BASED Statistical distribution of failure times Degradation models o Markov models o Filtering approaches (e.g. Kalman and Particle Filtering) o Conventional numerical algorithms o o Linear regression Time series analysis Machine learning and data mining algorithms o o o o Artificial neural networks Fuzzy logic systems Support vector machines
Model-based approach: particle filtering Crack Propagation in Rotating Machinery Kalman Filter Exact only for linear systems and additive Gaussian noises Extended-Kalman or Gaussian-sum Analytical approximation Grid-based filters Numerical approximation (burdensome) PARTICLE FILTERING Numerical approximation which, in the limit, tends to the exact p x z posterior pdf ( ) k 0: k
Available information Current historical path of degradation {z 1, z 2,, z N } Degradation model x = f ( x, ω ) Failure threshold k k k 1 k 1 z = h ( x, υ ) k k k k DATA & INFORMATION z k Hidden Markov process Measurament equation t p time HYPOTHESES: System model: x = hidden degradation state vector ω = i.i.d. random process noise vector f = non-linear dynamics function vector k = time step index Measurement equation: υ = i.i.d. random measurement noise vector h = non-linear measurement function vector *F. Cadini, E. Zio, D. Avram, Monte Carlo-based filtering for fatigue crack growth estimation, Probabilistic Engineering Mechanics, 24, pp. 367-373, 2009
Method TIME STEP K-1: p( x ) IS KNOWN k 1 z1 :k 1 i z k not yet collected Monte Carlo prediction of N state trajectories (= particles) i xk i = 1, K, N TIME STEP K i z k available Observation Likelihood (particle weights) i i p z k x k wk ( ) x = f ( x, ω ) k k k 1 k 1 System model BAYES RULE Posterior (updated) distribution of the system state x k z = h ( x, υ ) k k k k Measurement equation State estimate N i k z1 : k ) = w k δ i= 1 i ( x ) p( x x k k Failure time distribution i p( t z = w δ f 1 :k ) N i= 1 k i ( t t ) f
Results (I) Crack growth test pattern 100 90 80 70 State variable x 60 50 40 30 20 10 0 0 100 200 300 400 500 600 700 800 Time [min] 1000 900 800 700 600 RUL estimates RUL estimate RUL (Fuzzy Similarity) RUL estimate (Particle Filtering) RUL estimate Actual Remaining (Particle Life Filtering) Actual MTTF±1σ Remaining Life MTTF±1σ RUL [min] 500 400 300 200 100 0 0 100 200 300 400 500 600 700 800 900 1000 Time [min]
Results (II) Number of particles: 5000 Five measurements at time steps: k 1 = 100, k 2 = 200, k 3 = 300, k 4 = 400, k 5 = 400 ω k, υ k = Gaussian noises d * = 80 Crack depth evolution Expected cost per unit time 527 k 1 =100 527 k 2 =200 535 k 3 =300 568 k 4 =400 535 k 5 =500
Approaches Wide range of Data available Modeling schemes Processing algorithms Wide range of different approaches DATA-DRIVEN MODEL-BASED Statistical distribution of failure times Degradation models o o o o o Markov models Shock models General Path Models Particle Filtering Conventional numerical algorithms o o Linear regression Time series analysis Machine learning and data mining algorithms o o o o Artificial neural networks Fuzzy logic systems Support vector machines
Available information AVAILABLE DATA & INFORMATION Current historical path of degradation { f (1),..., f ( k) } Failure threshold HI time 100 degradation paths of data taken at successive inspection times
Method (I) On-line estimation of the available RUL in a developing accidental scenario, based on monitored signals related to its evolution Similarity-based approach for the prediction of RUL Library of reference trajectory patterns Data from failure dynamic scenarios of the system Fuzzy-similarity comparison prediction New developing accidental scenario Recovery Time E. Zio, F. Di Maio, A Data-Driven Fuzzy Approach for Predicting the Remaining Useful Life in Dynamic Failure Scenarios of a Nuclear System, Reliability Engineering and System Safety, RESS, Volume 95(1), Pages 49-57, 2010. E. Zio, F. Di Maio, M. Stasi, A Data-driven Approach for Predicting Failure Scenarios in Nuclear Systems, Annals of Nuclear Energy, 37, 482 491, 2010. E. Zio, F. Di Maio, A Fuzzy Similarity-Based Method for Failure Detection and Recovery Time Estimation, International Journal of Performability Engineering, Vol. 6, No. 5, September 2010.
Results Advantages: Accuracy of the RUL estimates Capability of uncertainty evaluation Short computational time 1000 900 800 RUL estimate Actual Remaining Life MTTF±1σ 700 RUL [min] 600 500 400 300 200 100 0 0 100 200 300 400 500 600 700 800 900 1000 Time [min]
APPLICATIONS 24
25 FAULT DETECTION TOPIC 1: NPP SENSOR CONDITION MONITORING
Practical Applications Of NPP Sensor Condition Monitoring 26 1. Condition monitoring and signal reconstruction of: a. 215 sensors at Loviisa Nuclear Power Plant (Finland, in collaboration with HRP) b. 792 sensors at OKG Nuclear Power Plant (Sweden, in collaboration with HRP) 2. Signal reconstruction in support to the control of the pressurizer of a nuclear power plant
Sensor condition monitoring: why and how Periodic off-line re-calibration of all the sensors (e.g. during plant outages) high costs difficult accessibility On-line sensor monitoring: evaluation of sensors performance and calibration status during plant operation
Sensor condition monitoring: objectives 28 Detection of sensor failures and de-calibrations On-line correction of the degraded information Failure Measured signal Physical quantity Sensor Reconstructed signal Accurate and robust signal reconstruction model Reduced maintenance costs Enhanced safety
Applications 29 Condition monitoring and signal reconstruction of: 215 sensors at Loviisa Nuclear Power Plant (Finland) [1,2] 792 sensors at OKG Nuclear Power Plant (Sweden) [1,3] [1] P. Baraldi, E. Zio, G. Gola, D. Roverso, M. Hoffmann, "Robust nuclear signal reconstruction by a novel ensemble model aggregation procedure", International Journal of Nuclear Knowledge Management, Vol 4 (1), pp. 34-41, 2010. [2] P. Baraldi, G. Gola, E. Zio, D. Roverso, M. Hoffmann, "A randomized model ensemble approach for reconstructing signals from faulty sensors". Expert Systems With Application, Vol. 38 (8), pp. 9211-9224, 2011 [3] P. Baraldi, E. Zio, G. Gola, D. Roverso, M. Hoffmann, "Two novel procedures for aggregating randomized model ensemble outcomes for robust signal reconstruction in nuclear power plants monitoring systems", Annals of Nuclear Energy, Vol. 38 (2-3), pp. 212-220, 2011.
Application a) 30 Loviisa PWR 215 OKG BWR 792
Results (1) 31 Reconstruction of a signal with offset Measured signal True signal value 12.1 LF reconstruction 12 Residual = sensor fault 11.9 11.8 11.7 11.6 11.5 11.4 0 100 200 300 400 500 600 700 800 900 1000
Application b) 32 Loviisa PWR 215 OKG BWR 792
Results 33 88 Reconstruction of a signal with linear drift Residual = sensor fault 87 86 85 84 83 82 81 80 Measured signal True signal value Median reconstruction 79 78 150 200 250 300 350 400 450 500
FAULT PROGNOSTICS 34
Applications 35 Application 1: Prognostics of turbine creep growth Application 2: Prognostics of thrust bearing vibration (Hong Kong, in collaboration with CityU) Application 3: Prognostics of failure scenarios in a LBE-XADS nuclear power plant Application 4: NPP steam generator (France, in collaboration with EDF) Application 5: Prognostics of Oil & Gas Valves (Norway, in collaboration with Halden Reactor Project) Application 6: Scale deposition in Oil & Gas Well equipment (Brasil, in collaboration with Petrobras and UFPE)
36 FAULT PROGNOSIS APPLICATION 6: SCALE DEPOSITION IN OIL & GAS WELL EQUIPMENT (in collaboration with Petrobras and UFPE)
7 Application 1: Scale Deposition in Oil Well Equipment* Equipment: tubulars and valves for offshore drilling Degradation mechanism: scale deposition Degradation state: thickness of scale deposition (x) not measurable during operation! Available information: 32 laboratory tests (z,x) MEASURED SIGNALS (z) Orientation Location Roughness Initial Weight Test Temperature Test Pressure Brine Concentration Test Duration DEGRADATION STATE (x) Scale Thickness * In collaboration with Petrobras, Brazil
Sensitivity Analysis 8 Which signals have an influence on the scale thickness? Basic Idea Output Influence Output No Influence Large Separation Small Variance Small values Large values Input Signal Small values Large values Input Signal Classification Tree 32 data Brine 28 4 Pressure Orientation 9 19 2 2...... 3 most influencing signals: Brine Pressure Orientation
Empirical Modelling: Ensemble of Neural Networks 9 Input Signals 3 most influencing measured signals 5 most influencing measured signals Neural Network 1 Neural Network 100 Neural Network 101 Neural Network 200 Median Scale Thickness (x) All measured signals Neural Network 201 Neural Network 300
Model performance 10
CONCLUSIONS 41
Prognostics and Health Management (PHM) 42 Evolution to failure Healthy
Prognostics and Health Management (PHM) 3 Evolution to failure Healthy Degradation initiation
Prognostics and Health Management (PHM) 3 Evolution to failure Healthy Degradation initiation Present time (t 0 )
Prognostics and Health Management (PHM) 3 Evolution to failure Healthy PHM tasks Degradation initiation Present time (t 0 ) Degradation evolution Failure
Prognostics and Health Management (PHM) 3 Evolution to failure Healthy PHM tasks Degradation initiation Present time (t 0 ) Degradation evolution Failure 1 Health assessment
Prognostics and Health Management (PHM) 3 Evolution to failure RUL (Remaining Useful Life) Healthy PHM tasks Degradation initiation Present time (t 0 ) Degradation evolution Failure 1 Health assessment 2 Prognostics
Prognostics and Health Management (PHM) 3 Evolution to failure RUL (Remaining Useful Life) Healthy PHM tasks Degradation initiation Present time (t 0 ) Degradation Failure 1 Health assessment 2 Prognostics 3 Maintenance planning
PERSPECTIVES 49
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