Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things » książka
Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things
ISBN-13: 9781119515333 / Angielski / Twarda / 2018 / 800 str.
Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things
ISBN-13: 9781119515333 / Angielski / Twarda / 2018 / 800 str.
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Preface
Acknowledgements
Acronyms
Chapter 1 Introduction to PHM
1.1 Reliability and Prognostics
1.2 PHM of Electronics
1.3 PHM Approaches
1.3.1 PoF–based Approach
1.3.2 Canaries
1.3.3 Data–Driven Approach
1.3.4 Fusion Approach
1.4 Implementation of PHM in a System of Systems
1.5 PHM in the Internet of Things (IoT) Era
1.5.1 IoT–Enabled PHM Applications: Manufacturing
1.5.2 IoT–Enabled PHM Applications: Energy Generation
1.5.3 IoT–Enabled PHM Applications: Transportation and Logistics
1.5.4 IoT–Enabled PHM Applications: Automobiles
1.5.5 IoT–Enabled PHM Applications: Medical Consumer Products
1.5.6 IoT–Enabled PHM Applications: Warranty Services
1.5.7 IoT–Enabled PHM Applications: Robotics
1.6Summary
1.7References
Chapter 2 Sensor Systems for PHM
2.1 Sensor and Sensing Principles
2.1.1 Thermal Sensors
2.1.2 Electrical Sensors
2.1.3 Mechanical Sensors
2.1.4 Chemical Sensors
2.1.5 Humidity Sensors
2.1.6 Biosensors
2.1.7 Optical Sensors
2.1.8 Magnetic Sensors
2.2 Sensor Systems for PHM
2.2.1 Parameters to Be Monitored
2.2.2 Sensor System Performance
2.2.3 Physical Attributes of Sensor Systems
2.2.4 Functional Attributes of Sensor Systems
2.2.5 Reliability
2.2.6 Availability
2.2.7 Cost
2.3 Sensor Selection
2.4 Examples of Sensor Systems for PHM Implementation
2.5 Emerging Trends in Sensor Technology for PHM
2.6 References
Chapter 3 Physics–of–Failure Approach to PHM
3.1 PoF–based PHM Methodology
3.2 Hardware Configuration
3.3 Loads
3.4 Failure Modes, Mechanisms, and Effects Analysis
3.4.1 Examples of FMMEA for Electronic Devices
3.5 Stress Analysis
3.6 Reliability Assessment and Remaining–Life Predictions
3.7 Outputs from PoF–based PHM
3.8 Caution and Concerns in the Use of PoF–based PHM
3.9 Combining PoF with Data–Driven Prognosis
3.10 References
Chapter 4 Machine Learning: Fundamentals
4.1 Types of Machine Learning
4.1.1 Supervised, Unsupervised, Semi–supervised, and Reinforcement Learning
4.1.2 Batch and Online Learning
4.1.3 Instance–based and Model–based Learning
4.2 Probability Theory in Machine Learning: Fundamentals
4.2.1 Probability Space and Random Variables
4.2.2 Distributions, Joint Distributions, and Marginal Distributions
4.2.3 Conditional Distributions
4.2.4 Independence
4.2.5 Chain Rule and Bayes Rule
4.3 Probability Mass Function and Probability Density Function
4.3.1 Probability Mass Function
4.3.2 Probability Density Function
4.4 Mean, Variance, and Covariance Estimation
4.4.1 Mean
4.4.2 Variance
4.4.3 Robust Covariance Estimation
4.5 Probability Distributions
4.5.1 Bernoulli Distribution
4.5.2 Normal Distribution
4.5.3 Uniform Distribution
4.6 Maximum Likelihood and Maximum a Posteriori Estimation
4.6.1 Maximum Likelihood Estimation
4.6.2 Maximum a Posteriori Estimation
4.7 Correlation and Causation
4.8 Kernel Trick
4.9 Performance Metrics
4.9.1 Diagnostic Metrics
4.9.2 Prognostic Metrics
4.10 References
Chapter 5 Machine Learning: Data Pre–processing
5.1 Data Cleaning
5.1.1 Missing Data Handling
5.2 Feature Scaling
5.3 Feature Engineering
5.3.1 Feature Extraction
5.3.2 Feature Selection
5.4 Imbalanced Data Handling
5.4.1 Sampling Methods for Imbalanced Learning
5.4.2 Effect of Sampling Methods for Diagnosis
5.5 References
Chapter 6 Machine Learning: Anomaly Detection
6.1 Introduction
6.2 Types of Anomalies
6.2.1 Point Anomalies
6.2.2 Contextual Anomalies
6.2.3 Collective Anomalies
6.3 Distance–based Methods
6.3.1 MD Calculation Using an Inverse Matrix Method
6.3.2 MD Calculation Using a Gram–Schmidt Orthogonalization Method
6.3.3 Decision Rules
6.4 Clustering–based Methods
6.4.1 K–Means Clustering
6.4.2 Fuzzy C–Means Clustering
6.4.3 Self–Organizing Maps
6.5 Classification–based Methods
6.5.1 One–class Classification
6.5.2 Multi–class Classification
6.6 Statistical Methods
6.6.1 Sequential Probability Ratio Test
6.6.2 Correlation Analysis
6.7 Anomaly Detection with No System s Health Profile
6.8 Challenges in Anomaly Detection
6.9 References
Chapter 7 Machine Learning: Diagnostics and Prognostics
7.1 Overview of Diagnosis and Prognosis
7.2 Techniques for Diagnostics
7.2.1 Supervised Machine Learning Algorithms
7.2.2 Ensemble Learning
7.2.3 Deep Learning
7.3 Techniques for Prognostics
7.3.1 Regression Analysis
7.3.2 Particle Filtering
7.4 References
Chapter 8 Uncertainty Representation, Quantification, and Management in Prognostics
8.1 Introduction
8.2 Sources of Uncertainty in PHM
8.3 Formal Treatment of Uncertainty in PHM
8.3.1 Problem 1: Uncertainty Representation and Interpretation
8.3.2 Problem 2: Uncertainty Quantification
8.3.3 Problem 3: Uncertainty Propagation
8.3.4 Problem 4: Uncertainty Management
8.4 Uncertainty Representation and Interpretation
8.4.1 Physical Probabilities and Testing–based Prediction
8.4.2 Subjective Probabilities and Condition–based Prognostics
8.4.3 Why is RUL Prediction Uncertain?
8.5 Uncertainty Quantification and Propagation for RUL Prediction
8.5.1 Computational Framework for Uncertainty Quantification
8.5.2 RUL Prediction: An Uncertainty Propagation Problem
8.5.3 Uncertainty Propagation Methods
8.6 Uncertainty Management
8.7 Case Study: Uncertainty Quantification in the Power System of an Unmanned Aerial Vehicle
8.7.1 Description of the Model
8.7.2 Sources of Uncertainty
8.7.3 Results: Constant Amplitude Loading Conditions
8.7.4 Results: Variable Amplitude Loading Conditions
8.7.5 Discussion
8.8 Existing Challenges
8.8.1 Timely Predictions
8.8.2 Uncertainty Characterization
8.8.3 Uncertainty Propagation
8.8.4 Capturing Distribution Properties
8.8.5 Accuracy
8.8.6 Uncertainty Bounds
8.8.7 Deterministic Calculations
8.9 Summary
8.10 References
Chapter 9 PHM Cost and Return on Investment
9.1 Return on Investment
9.1.1 PHM ROI Analysis
9.1.2 Financial Costs
9.2 PHM Cost–Modeling Terminology and Definitions
9.3 PHM Implementation Costs
9.3.1 Nonrecurring Costs
9.3.2 Recurring Costs
9.3.3 Infrastructure Costs
9.3.4 Nonmonetary Consideration and Maintenance Culture
9.4 Cost Avoidance
9.4.1 Maintenance Planning Cost Avoidance
9.4.2 Discrete–Event Simulation Maintenance Planning Model
9.4.3 Fixed–Schedule Maintenance Interval
9.4.4 Data–Driven (Precursor to Failure Monitoring) Methods
9.4.5 Model–Based (LRU–Independent) Methods
9.4.6 Discrete–Event Simulation Implementation Details
9.4.7 Operational Profile
9.5 Example PHM Cost Analysis
9.5.1 Single–Socket Model Results
9.5.2 Multiple–Socket Model Results
9.6 Example Business Case Construction: Analysis for ROI
9.7 Summary
9.8 References
Chapter 10 Valuation and Optimization of PHM–Enabled Maintenance Decisions
10.1 Valuation and Optimization of PHM–Enabled Maintenance Decisions for an Individual System
10.1.1 A PHM–Enabled Predictive Maintenance Optimization Model for an Individual System
10.1.2 Case Study: Optimization of PHM–Enabled Maintenance Decisions for an Individual
System (Wind Turbine)
10.2 Availability
10.2.1 The Business of Availability: Outcome–Based Contracts
10.2.2 Incorporating Contract Terms into Maintenance Decisions
10.2.2 Case Study: Optimization of PHM–Enabled Maintenance Decisions for Systems (Wind Farm)
10.3 Future Directions
10.3.1 Design for Availability
10.3.2 Prognostics–Based Warranties
10.3.3 Contract Engineering
10.4 References
Chapter 11 Health and Remaining Useful Life Estimation of Electronic Circuits
11.1 Introduction
11.2 Related Work
11.2.1 Component–Centric Approach
11.2.2 Circuit–Centric Approach
11.3 Electronic Circuit Health Estimation Through Kernel Learning
11.3.1 Kernel–Based Learning
11.3.2 Health Estimation Method
11.3.3 Implementation Results
11.4 RUL Prediction Using Model–Based Filtering
11.4.1 Prognostics Problem Formulation
11.4.2 Circuit Degradation Modeling
11.4.3 Model–Based Prognostic Methodology
11.4.4 Implementation Results
11.5 Summary
11.6 References
Chapter 12 PHM–based Qualification of Electronics
12.1 Why is Product Qualification Important?
12.2 Considerations for Product Qualification
12.3 Review of Current Qualification Methodologies
12.3.1 Standards–Based Qualification
12.3.2 Knowledge–Based or Physics–of–Failure–Based Qualification
12.3.3 Prognostics and Health Management–Based Qualification
12.4 Summary
12.3 References
Chapter 13PHM of Li–ion Batteries
13.1 Introduction
13.2 State of Charge Estimation
13.2.1 Case Study I: SOC Estimation
13.2.2 Case Study II: SOC Estimation
13.3 State of Health Estimation and Prognostics
13.3.1 Case Study for Li–ion Battery Prognostics
13.4 Summary
13.5 References
Chapter 14 PHM of Light–Emitting Diodes
14.1 Introduction
14.2 Review of PHM Methodologies for LEDs
14.2.1 Overview of Available Prognostic Methods
14.2.2 Data–Driven Methods
14.2.3 Physics–Based Methods
14.2.4 LED System–Level Prognostics
14.3 Simulation–Based Modeling and Failure Analysis for LEDs
14.3.1 LED Chip–Level Modeling and Failure Analysis
14.3.2 LED Package–Level Modeling and Failure Analysis
14.3.3 LED System–Level Modeling and Failure Analysis
14.4 Return–on–Investment Analysis of Applying Health Monitoring to LED Lighting Systems
14.4.1 ROI Methodology
14.4.2 ROI Analysis of Applying System Health Monitoring to LED Lighting Systems
14.5 Summary
14.6 References
Chapter 15 PHM of Healthcare
15.1 Healthcare in the U.S.
15.2 Considerations in Healthcare
15.2.1 Clinical Considerations in Implantable Medical Devices
15.2.2 Considerations in Care Bots
15.3 Benefits of PHM
15.3.1 Safety Increase
15.3.2 Operational Reliability Improvement
15.3.3 Mission Availability Increase
15.3.4 System s Service Life Extension
15.3.5 Maintenance Effectiveness Increase
15.4 PHM of Implantable Medical Devices
15.5 PHM of Care Bots
15.6 Canary–based Prognostics of Healthcare Devices
15.7 Summary
15.8 References
Chapter 16 PHM of Subsea Cables
16.1 Subsea Cable Market
16.2 Subsea Cables
16.3 Cable Failures
16.3.1 Internal Failures
16.3.2 Early–Stage Failures
16.3.3 External Failures
16.3.4 Environmental Conditions
16.3.5 Third–Party Damage
16.4 State–of–the–Art Monitoring
16.5 Qualifying and Maintaining Subsea Cables
16.5.1 Qualifying Subsea Cables
16.5.2 Mechanical Tests
16.5.3 Maintaining Subsea Cables
16.6 Data–Gathering Techniques
16.7 Measuring the Wear Behavior of Cable Materials
16.8 Predicting Cable Movement
16.8.1 Sliding Distance Deviation
16.8.2 Scouring Depth Calculations
16.9 Predicting Cable Degradation
16.9.1 Volume Loss Due to Abrasion
16.9.2 Volume Loss Due to Corrosion
16.10 Predicting Remaining Useful Life
16.11 Case Study
16.12 Future Challenges
16.12.1 Data–Driven Approach for Random Failures
16.12.2 Model–Driven Approach for Environmental Failures
16.13 Summary
16.14 References
Chapter 17 Connected Vehicle Diagnostics and Prognostics
17.1 Introduction
17.2 Design of an Automatic Field Data Analyzer
17.2.1 Data Collection Subsystem
17.2.2 Information Abstraction Subsystem
17.2.3 Root Cause Analysis Subsystem
17.3 Case Study: CVDP for Vehicle Batteries
17.3.1 Brief Background of Vehicle Batteries
17.3.2 Applying AFDA for Vehicle Batteries
17.3.3 Experimental Results
17.4 Summary
17.5 References
Chapter 18 The Role of PHM at Commercial Airlines
18.1 Evolution of Aviation Maintenance
18.2 Stakeholder Expectations for PHM
18.2.1 Passenger Expectations
18.2.1 Airline/Operator/Owner Expectations
18.2.2 Airframe Manufacturer Expectations
18.2.3 Engine Manufacturer Expectations
18.2.4 System and Component Supplier Expectations
18.2.5 MRO Organization Expectations
18.3 PHM Implementations
18.3.1 SATAA
18.4 PHM Applications
18.4.1 Engine Health Management
18.4.2 Auxiliary Power Unit (APU) Health Management
18.4.3 Environmental Control System (ECS) and Air Distribution Health Monitoring
18.4.4 Landing Systems Health Monitoring
18.4.5 Liquid Cooling Systems Health Monitoring
18.4.6 Nitrogen Generation System (NGS) Health Monitoring
18.4.7 Fuel Consumption Monitoring
18.4.8 Flight Control Systems Health Monitoring
18.4.9 Electric Power Systems Health Monitoring
18.4.10 Structural Health Monitoring (SHM)
18.4.11 Fuel and Hydraulic Systems Health Management
18.5 Summary
18.6 References
Chapter 19 PHM Software of Electronics
19.1 PHM Software: CALCE Simulation Assisted Reliability Assessment
19.2 PHM Software: Data–Driven
19.1.1 Data Flow
19.1.2 Master Options
19.1.3 Data Pre–processing
19.1.4 Feature Discovery
19.1.5 Anomaly Detection
19.1.6 Diagnostics/Classification
19.1.7 Prognostics/Modeling
19.1.8 Challenges in Data–Driven PHM Software Development
19.3 Summary
Chapter 20 eMaintenance
20.1 From Reactive to Proactive Maintenance
20.2 The Onset of eMaintenance
20.3 Maintenance Management System
20.3.1 Lifecycle Management
20.3.2 eMaintenance Architecture
20.4 Sensor Systems
20.5 Data Analysis
20.6 Predictive Maintenance
20.7 Maintenance Analytics
20.7.1 Maintenance Descriptive Analytics
20.7.2 Maintenance Analytics and eMaintenance
20.7.3 Maintenance Analytics and Big Data
20.8 Knowledge Discovery
20.9 Integrated Knowledge Discovery
20.10 User Interface for Decision Support
20.11 Applications of eMaintenance
20.11.1 eMaintenance in Railways
20.11.2 eMaintenance in Manufacturing
20.11.3 MEMS Sensors for Bearing Vibration Measurement
20.11.4 Wireless Sensors for Temperature Measurement
20.11.5 Monitoring Systems
20.11.6 eMaintenance Cloud and Servers
20.11.7 Dashboard Managers
20.11.8 Alarm Servers
20.11.9 Cloud Services
20.11.10 Graphic User Interfaces
20.12 Internet Technology and Optimizing Technology
20.13 References
Chapter 21 Predictive Maintenance in the IoT Era
21.1 Background
21.1.1 Challenges of a Maintenance Program
21.1.2 Evolution of Maintenance Paradigms
21.1.3 Preventive vs. Predictive Maintenance
21.1.4 P–F Curve
21.1.5 Bathtub Curve
21.2 Benefits of a Predictive Maintenance Program
21.3 Prognostic Model Selection for Predictive Maintenance
21.4 Internet of Things
21.4.1 Industrial IoT
21.5 Predictive Maintenance Based on IoT
21.6 Predictive Maintenance Usage Cases
21.7 Machine Learning Techniques for Data–Driven Predictive Maintenance
21.7.1 Supervised Learning
21.7.2 Unsupervised Learning
21.7.3 Anomaly Detection
21.7.4 Multiclass and Binary Classification Models
21.7.5 Regression Models
21.7.6 Survival Models
21.8 Best Practices
21.8.1 Define Business Problem and Quantitative Metrics
21.8.2 Identify Assets and Data Sources
21.8.3 Data Acquisition and Transformation
21.8.4 Build Models
21.8.5 Model Selection
21.8.6 Predict Outcomes and Transform into Process Insights
21.8.7 Operationalize and Deploy
21.8.8 Continuous Monitoring
21.9 Challenges in a Successful Predictive Maintenance Program
21.9.1 Predictive Maintenance Management Success Key Performance Indicators (KPIs)
21.10 Summary
21.11 References
Chapter 22 Analysis of PHM Patents for Electronics
22.1 Introduction
22.2 Analysis of PHM Patents for Electrical Systems
22.2.1 Sources of PHM Patents
22.2.2 Analysis of PHM Patents
22.3 Trend of PHM Activities for Electrical Systems in Industries
22.2.1 Sources of PHM Patents
22.2.2 Batteries
22.2.3 Electric Motors
22.2.4 Circuits and Systems
22.2.5 Electrical Devices in Automobiles and Airplanes
22.2.6 Networks and Communication Facilities
22.2.7 Others
22.4 Trend of PHM Activities for Electrical Systems in Academia
22.5 Gap of Viewpoint on PHM between Industries and Academia
22.6 Summary
22.7 References
Chapter 23 A PHM Roadmap for Electronics–Rich Systems
23.1 Introduction
23.2 Roadmap Classifications
23.2.1 Component–level PHM
23.2.2 System–level PHM
23.3 Methodology Development
23.3.1 Best Algorithms
23.3.2 Verification and Validation
23.3.3 Log–Term PHM Studies
23.4 PHM for Storage
23.5 PHM for No–Fault Found/Intermittent Failures
23.6 PHM for Products Subjected to Indeterminate Operating Conditions
23.7 Nontechnical Barriers
23.7.1 Cost, ROI, Business Case Development
23.7.2 Liability and Litigation
23.7.3 Maintenance Culture
23.7.4 Contract Structure
23.7.5 Role of Standards Organizations
23.7.6 Licensing and Entitlement Management
23.8 References
Appendix ACommercially Available Sensor Systems for PHM
Appendix BJournals and Conference Proceedings Related to PHM
Appendix C Glossary of Terms and Definitions
Index
Michael G. Pecht, PhD, is Chair Professor in Mechanical Engineering and Professor in Applied Mathematics, Statistics and Scientific Computation at the University of Maryland. He is the Founder and Director of the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland, which is funded by more than 150 leading electronics companies. Dr. Pecht is an IEEE, ASME, SAE, and IMAPS Fellow and serves as editor–in–chief of IEEE Access. He has written more than 30 books, 700 technical articles, and has 8 patents.
Myeongsu Kang, PhD, is currently a Research Associate at the Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA. His expertise is in data analytics, machine learning, system modeling, and statistics for prognostics and systems health management. He has authored/coauthored more than 60 publications in leading journals and conference proceedings.
An indispensable guide for engineers and data scientists in design, testing, operation, manufacturing, and maintenance
A road map to the current challenges and available opportunities for the research and development of Prognostics and Health Management (PHM), this important work covers all areas of electronics and explains how to:
- assess methods for damage estimation of components and systems due to field loading conditions
- assess the cost and benefits of prognostic implementations
- develop novel methods for in situ monitoring of products and systems in actual life–cycle conditions
- enable condition–based (predictive) maintenance
- increase system availability through an extension of maintenance cycles and/or timely repair actions;
- obtain knowledge of load history for future design, qualification, and root cause analysis
- reduce the occurrence of no fault found (NFF)
- subtract life–cycle costs of equipment from reduction in inspection costs, downtime, and inventory
Prognostics and Health Management of Electronics also explains how to understand statistical techniques and machine learning methods used for diagnostics and prognostics. Using this valuable resource, electrical engineers, data scientists, and design engineers will be able to fully grasp the synergy between IoT, machine learning, and risk assessment.
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