Sustainment Systems Division Model-based Diagnostics, Prognostics & Health Management.
Chapter 1. Introduction : Prognostics & Health Management
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Seoul National University Prognostics and Health Management (PHM) Chapter 1. Introduction : Prognostics & Health Management Byeng D. Youn System Health & Risk Management Laboratory Department of Mechanical & Aerospace Engineering Seoul National University
Transcript of Chapter 1. Introduction : Prognostics & Health Management
Seoul National University
Prognostics and Health Management (PHM)
Chapter 1. Introduction: Prognostics & Health Management
Byeng D. YounSystem Health & Risk Management LaboratoryDepartment of Mechanical & Aerospace EngineeringSeoul National University
Seoul National University
CONTENTS
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Motivation1PHM Overview2FMECA, ALT, and NDE3Key PHM Edges4
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Chapter 1. Introduction
Still Physical Systems Fail…
8100TEU containership sinking, June 17, 2013
- Due to Buckling of shell plating & Fatigue in welded structure- Consequence: about $500 million property & business loss
LNG plant explosion, Jan. 19, 2004- Due to LNG Leak in Pipe- Consequence: 27 killed, 72 injured, $100 million loss
Consequence: U.S. solely spends $250 billion/year on reliability and maintenance in 2012
Wind turbine failure, Feb. 22 2008- Due to Maintenance error (brake failure)- Consequence : Collapse of whole wind turbine
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Maintenance
Corrective Maintenance (Reactive maintenance)• Corrective maintenance (also known as "breakdown maintenance") is a maintenance action executed in the
event of equipment breakdown.• Corrective maintenance focuses on restoring the equipment to its normal operating condition. The breakdown
equipment is returned to normal within service specifications by replacing or repairing faulty parts and components.
• Pros: Reduce unnecessary preventive maintenance action, relevant to the non-critical componentCons: Unable to prepare for the breakdown in advance, not applicable to the critical components
Preventive Maintenance• Preventive maintenance is any maintenance that is designed to retain the healthy condition of equipment and
prevent failure.• Scheduled maintenance: Maintain based on a planned schedule• Pros: Prevent the critical accident in advance and reduce the maintenance and downtime cost
Cons: Require accurate condition diagnosis system
Proactive maintenance• Proactive maintenance is the maintenance philosophy that supplants “failure reactive” with “failure proactive”
by activities that avoid the underlying conditions that lead to faults and degradation.• Condition-based (or predictive) maintenance: Maintain when anomaly condition is detected or predicted• Pros: No failure on the system, prolong system lifetime,
Cons: Require thorough understanding of the system, difficult to predict the states of the system.
Chapter 1. Introduction
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What PHM Can Do?
• Health degradation by aging• System reliability reduce• Failure rate increase
• Lack of system verification• Harsh operating condition for
high efficiencyInfant failure detectionPrognostics of random failures
Anticipation of failures due to agingSmart O&M decision making
PHM(Prognostics & Health Management)
System Failure New SystemDegradation
Chapter 1. Introduction
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Concept of PHM
- Health degradation due to aging- Prevention of health worsening by
proper diagnosis and treatment
- Performance degradation - Failure prevention and life extension
with PHM technique
Human lifetime
Haz
ard
Rat
e o
rH
ealth
con
ditio
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Cost of medicineProbability of death
Human PHM Action
Sensing ... Management
Human Engineering System
Chapter 1. Introduction
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Chapter 1. Introduction
PHM Procedure
Sensing module
- To ensure high damage detectability by designing an optimal sensor network
Reasoning module
Prognostics module Management module
Failure Analysis PHM Solution
Validation
- To extract system health relevant information in real-time and to classify system health condition
- To predict remaining useful lives (RULs) of engineered systems in real-time
- To enable optimal decision making on maintenance of engineered systems
- Understanding system failure
- FMECA- ALT- NDE
- Validation metric for detection, diagnosis, and prognosis
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1. Failure Analysis
FMECA – Failure Modes Effects and Criticality Analysis • Failure Modes and Effects Analysis (FMECA) is methodology for analyzing causes of
failures and understanding their frequency and impact. • Failure mode: Effect by which a failure is observed on failed parts or systems.
– Ex. Bearing failure mode: excessive wear, breakage, etc.• The FMECA result highlights failure modes with relatively high probability and severity
of consequences, allowing remedial effort to be directed where it will produce the greatest value.
• The FMECA includes,– Failure mode identification– Failure effects analysis– Severity classification– Failure detection methods– Criticality ranking– Critical item/failure mode list– Maintainability analysis
• Understanding system failure is important to develop the reliable PHM algorithm.
Chapter 1. Introduction
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1. Failure Analysis
FMECA – Failure Modes Effects and Criticality Analysis
• Understanding system failure is important to develop the reliable PHM algorithm.
Chapter 1. Introduction
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1. Failure Analysis• Failure matrix
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1. Failure AnalysisALT – Accelerated Life Testing• Testing a product by subjecting it to conditions (stress, strain, temperatures, voltage,
vibration rate, pressure etc.) in excess of its normal service parameters in an effort to uncover faults and potential modes of failure in a short amount of time.
• By analyzing the product's response to such tests, engineers can make predictions about the service life and maintenance intervals of a product.
NDE – Non-destructive Evaluation• NDE is a wide group of analysis techniques used in science and technology industry to
evaluate the properties of a material, component or system without causing damage.• The frequently used NDE methods are
– Eddy-current– Magnetic-particle– Liquid penetrant– Radiographic– Ultrasonic– Visual testing
Chapter 1. Introduction
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1. Sensing Solution 2. Reasoning Solution
3. Diagnostics and Prognostics Solution 4. Management Solution
2. PHM Solutions
: Design sensor network maximizing the health information
: Estimation and classification of current health state of a system
• Data acquisition planning• Selection of sensor type & number• Sensor network design
• Health data & health index• Time- & frequency analysis• Time-frequency analysis• Signal processing• Physics-based approach• AI-based approach
• Rule-based diagnostics• Physics-based diagnostics• AI-based diagnostics• Remaining useful life (RUL) prediction• Physics-based approach• Data-driven approach
: Predictive diagnostics and prediction of future state of system
• Definition of health classes (normal/warning/fault/failure) and corresponding maintenance actions
• Corrective/preventive/predictive maintenance
• Maintenance scheduling strategies• Resilience-driven design• Cost analysis
: Optimal system operation and maintenance
Chapter 1. Introduction
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Chapter 1. Introduction
Domain Knowledge
Data-driven
Rule-based Physics-based
Data-driven + Physics-based
Dat
a Si
ze
Health Features
Raw Data
Feature Engineering
Preprocessing
Diagnostics/Prognostics
Health Prediction
Power & Energy
Hydro-system
Electrical &Electronics
Power Trans.
Machining
Driving
Cleaning Filtering
EnvelopingScalingStatistical Modeling
Organization
Domain Knowledge
Data-driven
Rule-based Physics-based
Data-driven + Physics-based
Dat
a Si
zeDomain Knowledge
Data-driven
Rule-based Physics-based
Data-driven + Physics-based
(Hybrid)
Dat
a Si
ze
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2.1 Sensing SolutionSelection of failure modes and sensor types
• Sensor network design and IT solutions– System characteristics – Operating and environmental conditions– Sensor location– Number of sensors– Sampling rate & duration– DAQ, gateway & communication– Server and cloud
Acquisition of normal and failure data • Real system– Hard to acquire failure data
• Testbed – Easy to implement failure modes
• CAE model – To help understanding of system physics
Failure modes
Sensor types
HIP LP-A LP-B
Brn-1 Brn-2 Brn-3 Brn-4 Brn-5 Brn-6 Brn-7
Chapter 1. Introduction
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2.2 Reasoning SolutionData Preprocessing & Processing
Time- & frequency analysis Time-frequency analysis
|Y|
Frequency
Time
Y(t)
Spectrogram
1 2 3 4
Time (secs)
0
0.5
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Freq
uenc
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Hz)
-60
-40
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0
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(dB/
Hz)
Chapter 1. Introduction
Preprocessing
- Filtering (High/low/band, AR, MED, etc.)
- Denoising (TSA, resampling, etc.)
- Enveloping- Scaling
Deep learning-based feature engineering
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2.3.1 Diagnostics Solution
Chapter 1. Introduction
Diagnostics
Supervised classification – with labeled data
X1
X 2
Class IClass II
X1
X 2
Cluster ICluster II
Unsupervised classification – without labeled data
Rule-based classification
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2.3.2 Prognostics SolutionPhysics-based Approach• Pros
– Possible to assess RUL in early stages– Understanding of PoF
• Cons– Unreliable prediction accuracy– Applied to component level– Physics of failure (PoF) knowledge
required
Simulation Estimation
Identify Model
Simulate with Loading
Loading Signals
Response Signals
Update Parameters
Update & Project HI
Predicted RUL
Offline Process Online Process
Extract Offline HI
Build Health Knowledge
Training Signals
Testing Signals
Extract Online HI
Project or Interpolate
Predicted RUL
Data-driven Approach• Pros
– Applicable to system level– Management of uncertainty
• Cons– Failure data necessary– Massive run-to-failure data required
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2.4 Management SolutionMaintenance Management• To guide maintenance activities and prepare maintenance resources• Corrective maintenance, preventive maintenance, predictive maintenance, etc.
Asset Management• Asset management involves the balancing of costs, opportunities and risks against the
desired performance of assets, to achieve the organizational objectives. (ISO 55000)• LCC (Life Cycle Cost) Problem
Maintenance cost Downtime for maintenance Efficiency of the asset Operating cost Quality of operation or service Safety to staff and the public
Resilience• An ability to sustain functionality by resisting and recovering from adverse events
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Chapter 1. Introduction
3. ValidationValidation Metric• To check the validity of the diagnostics and prognostics algorithms, the validation metrics are
needed.• Model validation metrics have been developed to provide a quantitative measure that
characterizes the agreement between predictions and observations.• Classical hypothesis testing
t-test statistic F-test statistic Anderson-Darling test Kolmogorov-Smirnov (K-S) test
• Bayes factor: is based on the Bayesian hypothesis testing. It is the ratio of posterior distributions of the null and alterative hypothesis.
• Frequentist’s metric: quantifies the agreement from a different perspective by measuring the distance between the mean of the predictions and the estimated mean of the physical observations.
• Area metric: measures the differences between the entire distributions from the observations and predictions. It can be used when only a few data points from predictions or experiments are available
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Chapter 1. Introduction
Health diagnostics and prognostics of power transformers against mechanical faultsVi
brat
ion
Mea
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.He
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Gra
de S
yste
m
0 10 200
20
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Oper. time [year]
120H
z m
ax.v
el. [
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Oper. time [year]
240H
z m
ax.v
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whole sensorsdesigned SN
α β
1 2 3 654 1 2 3 654replaced
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(yea
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Transformer unit #He
alth
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gnos
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Opt
imal
Sen
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ositi
onin
gTime-domain
0 500 1000 1500 20000
0.5
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X: 120Y: 3.33
X: 240Y: 0.7207
X: 360Y: 0.3368
Freq.-domain
PHM Examples
Optimal sensor positioning, Health grade system, Remaining useful life prediction
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Analyzed major failure mechanisms of machine tool spindle and applied them to the accelerated life testing, identified detection technique of major causes of failures
Accelerated life testing
0 100 200 300 400 500 600 7000
1
2
3Envelope Detection : Hilbert Transform
Frequency (Hz)
|X(f)
|
0 100 200 300 400 500 600 7000
0.5
1
Frequency (Hz)
|Y(f)
|
112.5Hz
218.8Hz
112.5Hz
218.8Hz
BSF_1x
BPFI_1x
BSF_1x
BPFI_1x
Data acquisition of Testbed
Failure analysis
Diagnosis for main components
Acce
lera
ted
Life
Te
stFa
ult D
iagn
osis
Zero failure reliability test
Anomaly detection
Threshold
Qualification Life
Design Life
X XX
X
Performance Evaluation
PHM Examples
Chapter 1. Introduction
Machine tool spindle failure mechanism-based accelerated life testing method and diagnostics algorithm
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THANK YOUFOR LISTENING
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Reference
[1] Stamatis, Dean H. Failure mode and effect analysis: FMEA from theory to execution. ASQ Quality Press, 2003.
[2] Spencer, F. W. (1991). "Statistical Methods in Accelerated Life Testing“. Technometrics. 33 (3): 360-362.
[3] Introduction to Nondestructive Testing, The American Society for Nondestructive Testing.
