Prognostics and Health Management

A comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life.

Prognostics and Health Management provides an authoritative guide for an understanding of the rationale and methodologies of a practical approach for improving system reliability using conditioned-based data (CBD) to the monitoring and management of health of systems. This proven approach uses electronic signatures extracted from conditioned-based electrical signals, including those representing physical components, and employs processing methods that include data fusion and transformation, domain transformation, and normalization, canonicalization and signal-level translation to support the determination of predictive diagnostics and prognostics.

Written by noted experts in the field, Prognostics and Health Management clearly describes how to extract signatures from conditioned-based data using conditioning methods such as data fusion and transformation, domain transformation, data type transformation and indirect and differential comparison. This important resource:

• Integrates data collecting, mathematical modelling and reliability prediction in one volume

• Contains numerical examples and problems with solutions that help with an understanding of the algorithmic elements and processes

• Presents information from a panel of experts on the topic

• Follows prognostics based on statistical modelling, reliability modelling and usage modelling methods

Written for system engineers working in critical process industries and automotive and aerospace designers, Prognostics and Health Management offers a guide to the application of condition-based data to produce signatures for input to predictive algorithms to produce prognostic estimates of functional health and life.

Autorentext

Douglas Goodman is Founder and Chief Engineer of Ridgetop Group, Inc., Arizona, USA. James P. Hofmeister is Distinguished Engineer, Advanced Research Group, Ridgetop Group, Inc., Arizona, USA. Ferenc Szidarovszky, Ph.D, is Senior Researcher, Advanced Research Group, Ridgetop Group, Inc., Arizona, USA.

Klappentext
PROGNOSTICS AND HEALTH MANAGEMENT A Practical Approach to Improving System Reliability Using Condition-Based Data A comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life Prognostics and Health Management provides an authoritative guide for an understanding of the rationale and methodologies of a practical approach for improving system reliability using condition-based data (CBD) to the monitoring and management of health of systems. This proven approach uses electronic signatures extracted from condition-based electrical signals, including those representing physical components, and employs processing methods that include data fusion and transformation, domain transformation, and normalization, canonicalization and signal-level translation to support the determination of predictive diagnostics and prognostics. This important resource:

• Integrates data collecting, mathematical modelling and reliability prediction in one volume
• Contains numerical examples and problems with solutions that help with an understanding of the algorithmic elements and processes
• Presents information from a panel of experts on the topic
• Follows prognostics based on statistical modelling, reliability modelling and usage modelling methods Written for system engineers working in critical process industries and automotive and aerospace designers, Prognostics and Health Management offers a guide to the application of condition-based data to produce signatures for input to predictive algorithms to produce prognostic estimates of functional health and life.

Inhalt
List of Figures xi

Series Editor's Foreword xxi

Preface xxiii

Acknowledgments xxvii

1 Introduction to Prognostics 1

1.1 What Is Prognostics? 1

1.1.1 Chapter Objectives 1

1.1.2 Chapter Organization 3

1.2 Foundation of Reliability Theory 3

1.2.1 Time-to-Failure Distributions 4

1.2.2 Probability and Reliability 6

1.2.3 Probability Density Function 7

1.2.4 Relationships of Distributions 10

1.2.5 Failure Rate 10

1.2.6 Expected Value and Variance 16

1.3 Failure Distributions Under Extreme Stress Levels 18

1.3.1 Basic Models 18

1.3.2 Cumulative Damage Models 21

1.3.3 General Exponential Models 21

1.4 Uncertainty Measures in Parameter Estimation 23

1.5 Expected Number of Failures 26

1.5.1 Minimal Repair 26

1.5.2 Failure Replacement 28

1.5.3 Decreased Number of Failures Due to Partial Repairs 30

1.5.4 Decreased Age Due to Partial Repairs 30

1.6 System Reliability and Prognosis and Health Management 31

1.6.1 General Framework for a CBM-Based PHM System 32

1.6.2 Relationship of PHM to System Reliability 34

1.6.3 Degradation Progression Signature (DPS) and Prognostics 35

1.6.4 Ideal Functional Failure Signature (FFS) and Prognostics 37

1.6.5 Non-ideal FFS and Prognostics 41

1.7 Prognostic Information 41

1.7.1 Non-ideality: Initial-Estimate Error and Remaining Useful Life (RUL) 42

1.7.2 Convergence of RUL Estimates Given an Initial Estimate Error 44

1.7.3 Prognostic Distance (PD) and Convergence 45

1.7.4 Convergence: Figure of Merit (𝜒𝛼) 45

1.7.5 Other Sources of Non-ideality in FFS Data 46

1.8 Decisions on Cost and Benefits 47

1.8.1 Product Selection 47

1.8.2 Optimal Maintenance Scheduling 49

1.8.3 Condition-Based Maintenance or Replacement 54

1.8.4 Preventive Replacement Scheduling 55

1.8.5 Model Variants and Extensions 58

1.9 Introduction to PHM: Summary 60

References 60

Further Reading 62

2 Approaches for Prognosis and Health Management/Monitoring (PHM) 63

2.1 Introduction to Approaches for Prognosis and Health Management/Monitoring (PHM) 63

2.1.1 Model-Based Prognostic Approaches 63

2.1.2 Data-Driven Prognostic Approaches 63

2.1.3 Hybrid Prognostic Approaches 64

2.1.4 Chapter Objectives 64

2.1.5 Chapter Organization 64

2.2 Model-Based Prognostics 65

2.2.1 Analytical Modeling 66

2.2.2 Distribution Modeling 71

2.2.3 Physics of Failure (PoF) and Reliability Modeling 72

2.2.4 Acceleration Factor (AF) 74

2.2.5 Complexity Related to Reliability Modeling 76

2.2.6 Failure Distribution 78

2.2.7 Multiple Modes of Failure: Failure Rate and FIT 79

2.2.8 Advantages and Disadvantages of Model-Based Prognostics 79

2.3 Data-Driven Prognostics 80

2.3.1 Statistical Methods 80

2.3.2 Machine Learning (ML): Classification and Clustering 85

2.4 Hybrid-Driven Prognostics 90

2.5 An Approach to Condition-Based Maintenance (CBM) 92

2.5.1 Modeling of Condition-Based Data (CBD) Signatures 92

2.5.2 Comparison of Methodologies: Life Consumption and CBD Signature 92

2.5.3 CBD-Signature Modeling: An Illustration 93

2.6 Approaches to PHM: Summary 103

References 103

Further Reading 106

3 Failure Progression Signatures 107

3.1 Introduction to Failure Signatures 107

3.1.1 Chapter Objectives 107

3.1.2 Chapter Organization 108

3.2 Basic Types of Signatures 108

3.2.1 CBD Signature 109

3.2.2 FFP Signature 114

3.2.3 Transforming FFP into FFS 118 3.2.4 Transforming F...