Condition monitoring, fault diagnostics and …li/materials/lecture_USTB.pdfCondition Monitoring and Prognostics and Health Management 4 Detection Diagnostics Prognostics Condition

Condition Monitoring, Fault Diagnostics and Prognostics of Industrial Equipment

Enrico Zio

PROGNOSTICS AND HEALTH PROGNOSTICS AND HEALTH MANAGEMENT (MANAGEMENT (PHM)PHM)

PHM: what

PHM = DDP =

= + +Detect PredictDiagnose

Normal operation

f2f1RemainingUsefulLife

Condition Monitoring andPrognostics and Health Management

4

Detection Diagnostics Prognostics

Condition Monitoring

Detection Diagnostics Prognostics

Maintenance

Ideal Maintenance

Tm=Tf – dt

•Component’s life fully exploited•Unavailability and further failures due to maintenance actions are avoided

Tf= failure timeTm= maintenance time

Uncertainty and Maintenance

The failure mechanisms have uncertainty associated with their occurrencein time:

� Inherent physical randomness of the degradation and failureprocesses

� Model used to assess the performance of the system �

imprecise reproduction of realityEarly prediction

Late prediction

When to perform maintenance: non-trivial decision

Failure

Normal operation

f1

f2FAULT DETECTIONearly recognition

Diagnostics: fault detection and classificationPrognostics: lifetime estimation

Diagnostic system

PLifetime estimation

Prognostic system

PrognosisPrognosis

FAULTCLASSIFICATIONcorrect assignment

f1

f2

Measured signals

Forcing functions

Maintenance Intervention Approaches

Maintenance Intervention

Unplanned Planned

PHM

Corrective

Replacement or repair of failed units

Scheduled

Perform inspections, and possibly

repairs, following a predefined schedule

Condition-

based

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

Fault detection FailurePredicted Failure Predicted Failure

RUL

Early prediction

Late prediction

RUL

2. Short time needed for prediction

Fault detection Failure prediction Failure

Time needed for prediction << RUL Time available for corrective action

3. RobustnessAccurate RUL estimate

time

0 500 1000

0 500 1000

PROGNOSIS

IMPRECISE

INCOMPLETE

INPUT INFORMATION

HI

Desiderata from prognostics methods (2)

4. Confidence estimationRUL

td tf

confidence interval

time

HI

0 500 1000

0 500 1000

0 500 1000

0 500 1000

PROGNOSIS

IMPRECISE

INCOMPLETE

INPUT INFORMATION

Techniques (I) 12

Component

Methodology box

Component Health Assessment

ComponentData &

InformationFault •Detection•Diagnostics•Prognostics

Data processing/ feature extraction

Feature selection Fault detection Fault Diagnosis Failu re 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 KernelRegression

Supervised clustering methods

Particle Filtering

Spectral Analysis Random Feature Subset Evolving Clustering Classification Tree Neural Networks

Techniques (II)

Spectral Analysis Random Feature Subset Selection

Evolving Clustering Method

Classification Tree Neural Networks

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

DATA-DRIVEN

• Conventional numerical algorithmso Linear regression

Wide range of different approaches

Wide range of data & information

MODEL-BASED

• Statistical distribution of failure

Approaches

o Linear regressiono Time series analysis

• Machine learning and data mining algorithmso Artificial neural networkso Fuzzy logic systemso Support vector machineso …

• Statistical distribution of failure times

• Degradation modelso Markov modelso Filtering approaches (e.g.

Kalman and Particle Filtering )o …

Kalman Filter Extended-Kalman or Gaussian-sum Grid-based filters

Exact only for linear Numerical

Model-based approach: particle filtering

Crack Propagation in Rotating Machinery

Exact only for linear systems and additive

Gaussian noises

Analytical approximation

Numerical approximation (burdensome)

PARTICLE FILTERING

Numerical approximation which, in the limit, tends to the exactposterior pdf ( )0:k kp x z

• Current historical path of degradation {z1, z2, …, zN}

• Degradation model

• Failure threshold

DATA & INFORMATION

1 1( , )

k k k k− −=x f x ω

( , )k k k k

=z h x υ

Hidden Markov process

Measurament equation

zk

timetp

Available information

HYPOTHESES:

• System model:� x = hidden degradation state vector� ωωωω = i.i.d. random process noise vector� f = non-linear dynamics function vector� k = time step index

• Measurement equation:� υυυυ = 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

Observation Likelihood(particle weights)

Monte Carlo prediction of N state trajectories (= particles)

BAYES RULE

( )i ik k kp wz x1, ,i

k i N=x K

not yet collected availableikz i

kz

TIME STEP K-1: IS KNOWN

TIME STEP K

)|( 1 1k:1k zx −−p

Method

Posterior (updated) distribution of the system state

BAYES RULE

1 1( , )

k k k k− −=x f x ω( , )

k k k k=z h x υ

System modelMeasurement

equation

State estimate Failure time distribution

( )∑=

−=N

i

if

ikf ttwtp

11 )|( δk:z( )∑

=

−=N

i

ikk

ikk wp

11 )|( xxzx k: δ

kx

10

20

30

40

50

60

70

80

90

100

Sta

te v

aria

ble

x

Crack growth test pattern

900

1000

RUL estimate (Fuzzy Similarity)

RUL estimate (Particle Filtering)Actual Remaining Life

RUL estimate (Fuzzy Similarity)

RUL estimate (Particle Filtering)

RUL estimates

Results (I)

0 100 200 300 400 500 600 700 8000

10

Time [min]

0 100 200 300 400 500 600 700 800 900 10000

100

200

300

400

500

600

700

800

Time [min]

RU

L [m

in]

Actual Remaining Life

MTTF±1σ

RUL estimate (Particle Filtering)Actual Remaining Life

MTTF±1σ

• Number of particles: 5000

• Five measurements at time steps: k1 = 100, k2 = 200, k3 = 300, k4 = 400, k5 = 400

• ωk, υk = Gaussian noises

• d* = 80

Crack depth evolution Expected cost per unit time

k1=100527

Results (II)

k1=100

k2=200

k3=300

k4=400

k5=500

527

535

568

535

MODEL-BASED

Approaches

Data availableModeling schemesProcessing algorithms

Wide range of different approaches Wide range of

DATA-DRIVEN

• Conventional numerical algorithmsMODEL-BASED

• Statistical distribution of failure times

• Degradation modelso Markov modelso Shock modelso General Path Modelso Particle Filteringo …

• Conventional numerical algorithmso Linear regressiono Time series analysis

• Machine learning and data mining algorithmso Artificial neural networkso Fuzzy logic systemso Support vector machineso …

• Current historical path of degradation


{ }(1),..., ( )f f k

AVAILABLE DATA & INFORMATION

HI

Available information


time

100 degradationpaths of data taken

at successive inspection times

Similarity-based approach for the prediction of RUL

Data from failure dynamic scenarios of the system

Library of reference trajectory patterns

New developing accidental scenarioFuzzy-similarity comparison

On-line estimation of the available RUL in a developing accidental scenario, based on monitored signals related to its evolution

Method (I)

Recovery Time

scenarios of the system comparison

prediction

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.

700

800

900

1000

RUL estimateActual Remaining Life

MTTF±1σ

Advantages:• Accuracy of the RUL estimates• Capability of uncertainty evaluation• Short computational time

Results

0 100 200 300 400 500 600 700 800 900 10000

100

200

300

400

500

600

Time [min]

RU

L [m

in]

APPLICATIONS

24

FAULT DETECTION

25

TOPIC 1: NPP SENSOR CONDITION MONITORING

Practical Applications Of NPP Sensor Condition Monitoring

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 pressurizerof a nuclear power plant

26

of a nuclear power plant

Periodic off-line re-calibration of all the sensors (e.g. during plant outages)

• high costs

• difficult accessibility

Sensor condition monitoring: why and how

On-line sensor monitoring : evaluation of sensors’ performance and calibration status during plant operation

Sensor condition monitoring: objectives

• Detection of sensor failures and de-calibrations• On-line correction of the degraded information

28

Physical

quantity

Measured signal

Sensor

Failure

Accurate and robust

signal

reconstruction model

Reconstructed signal

Reduced maintenance costs

Enhanced safety

Applications

• 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]

29

[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.

Loviisa PWR 215

Application a) 30

OKG BWR 792

31

11.9

12

12.1

Measured signalTrue signal valueLF reconstruction

Results (1)

Reconstruction of a signal with offset

Residual = sensor fault

0 100 200 300 400 500 600 700 800 900 1000

11.4

11.5

11.6

11.7

11.8

11.9

Loviisa PWR 215

Application b) 32

OKG BWR 792

Results

Reconstruction of a signal with linear drift

85

86

87

88 Residual = sensor fault

33

150 200 250 300 350 400 450 50078

79

80

81

82

83

84

Measured signalTrue signal valueMedian reconstruction

34

FAULT PROGNOSTICS

Applications

• 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)

35

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)

FAULT PROGNOSIS

36

APPLICATION 6: SCALE DEPOSITION IN OIL & GAS WELL EQUIPMENT (in collaboration with Petrobras and UFPE)

7

MEASURED SIGNALS ( z)

Application 1: Scale Deposition in Oil Well Equipme nt*

Equipment : tubulars and valves for offshore drillingDegradation mechanism : scale depositionDegradation state : thickness of scale deposition (x)

not measurable during operation!Available information : 32 laboratory tests (z,x)

MEASURED SIGNALS ( z)

Orientation Test Temperature

Location Test Pressure

Roughness Brine Concentration

Initial Weight Test Duration

DEGRADATIONSTATE (x)

Scale Thickness

* In collaboration with Petrobras, Brazil

8Sensitivity Analysis

� Basic IdeaOutput

Large Separation

OutputInfluence No Influence

Which signals have an influence on the scale thickn ess?

Small values

Large values

Input Signal

Large Separation

Small Variance

Small values

Large values

Input Signal

� Classification Tree

Brine

Pressure

Orientation

28

4

9

19

2

2

32 data

3 most influencing signals:� Brine� Pressure� Orientation

...

...

9Empirical Modelling: Ensemble of Neural Networks

Neural Network 100

Neural Network

Neural Network 1

3most influencing

measured signals

Median

Input Signals

5

Scale Thickness (x)

Neural Network 101

Neural Network 200

Neural Network 201

Neural Network 300

5most influencing

measured signals

Allmeasured

signals

10Model performance

41

CONCLUSIONS

Prognostics and Health Management (PHM)42

Healthy

Evolution to… failure

Prognostics and Health Management (PHM)

Healthy Degradation initiation


3




Present time (t )time (t0)

Failure




Present time (t )

Degradation evolution

time (t0)PHM tasks




Present time (t )

FailureDegradation evolution

PHM taskstime (t0)

Health assessment1




FailurePresent time (t )

RUL (Remaining Useful Life)

Degradation evolution

PHM taskstime (t0)

Health assessment

Prognostics

1

2


HealthyDegradation

Degradation initiation


FailurePresent time (t )

RUL (Remaining Useful Life)

PHM taskstime (t0)

Health assessment

Prognostics

Maintenance planning

1

2

3

49

PERSPECTIVES

50

Documents

Condition monitoring, fault diagnostics and …li/materials/lecture_USTB.pdfCondition Monitoring and Prognostics and Health Management 4 Detection Diagnostics Prognostics Condition