Prognostics and Health Management of Electronics
Prognostics and Health Management of Electronics
1 Introduction
1.1 Reliability and Prognostics
1.2 PHM for Electronics
1.3 PHM Approaches
1.4 Implementation of PHM in a System of Systems
1.5 PHM in the Internet of Things (IoT) Era
1.6 Summary
2 Sensor Systems for PHM
2.1 Sensor and Sensing Principles
2.2 Sensor Systems for PHM
2.3 Sensor Selection
2.4 Examples of Sensor Systems for PHM Implementation
2.5 Emerging Trends in Sensor Technology for PHM
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 (FMMEA)
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
4 Machine Learning: Fundamentals
4.1 Types of Machine Learning
4.2 Probability Theory in Machine Learning: Fundamentals
4.3 Probability Mass Function and Probability Density Function
4.4 Mean, Variance, and Covariance Estimation
4.5 Probability Distributions
4.6 Maximum Likelihood and Maximum A Posteriori Estimation
4.7 Correlation and Causation
4.8 Kernel Trick
4.9 Performance Metrics
5 Machine Learning: Data Pre-processing
5.1 Data Cleaning
5.2 Feature Scaling
5.3 Feature Engineering
5.4 Imbalanced Data Handling
6 Machine Learning: Anomaly Detection
6.1 Introduction
6.2 Types of Anomalies
6.3 Distance-Based Methods
6.4 Clustering-Based Methods
6.5 Classification-Based Methods
6.6 Statistical Methods
6.7 Anomaly Detection with No System Health Profile
6.8 Challenges in Anomaly Detection
7 Machine Learning: Diagnostics and Prognostics
7.1 Overview of Diagnosis and Prognosis
7.2 Techniques for Diagnostics
7.3 Techniques for Prognostics
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
8.4 Uncertainty Representation and Interpretation
8.5 Uncertainty Quantification and Propagation for RUL Prediction
8.6 Uncertainty Management
8.7 Case Study: Uncertainty Quantification in the Power System of an Unmanned Aerial Vehicle
8.8 Existing Challenges
8.9 Summary
9 PHM Cost and Return on Investment
9.1 Return on Investment
9.2 PHM Cost-Modeling Terminology and Definitions
9.3 PHM Implementation Costs
9.4 Cost Avoidance
9.5 Example PHM Cost Analysis
9.6 Example Business Case Construction: Analysis for ROI
9.7 Summary
10 Valuation and Optimization of PHM-Enabled Maintenance
10.1 Valuation and Optimization of PHM-Enabled Maintenance Decisions for an Individual System
10.2 Availability
10.3 Future Directions
11 Health and Remaining Useful Life Estimation of Electronic Circuits
11.1 Introduction
11.2 Related Work
11.3 Electronic Circuit Health Estimation Through Kernel Learning
11.4 RUL Prediction Using Model-Based Filtering
11.5 Summary
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
12.4 Summary
13 PHM of Li-ion Batteries
13.1 Introduction
13.2 State of Charge Estimation
13.3 State of Health Estimation and Prognostics
13.4 Summary
14 PHM of Light-Emitting Diodes
14.1 Introduction
14.2 Review of PHM Methodologies for LEDs
14.3 Simulation-Based Modeling and Failure Analysis for LEDs
14.4 Return-on-Investment Analysis of Applying Health Monitoring to LED Lighting Systems
14.5 Summary
17 Connected Vehicle Diagnostics and Prognostics
17.1 Introduction
17.2 Design of an Automatic Field Data Analyzer
17.3 Case Study: CVDP for Vehicle Batteries
17.4 Summary