Prognostics and Health Management
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Prognostics and Health Management : A Practical Approach to Improving System Reliability Using Condition-Based Data

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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.
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Product details

  • Hardback | 384 pages
  • 170 x 249 x 25mm | 778g
  • Wiley-Blackwell
  • Hoboken, United States
  • English
  • 1. Auflage
  • 1119356652
  • 9781119356653

Back cover copy

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.
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Table of contents

CHAPTER 1: Introduction to Prognostics

1.1 What is Prognostics? 1

1.1.1 Objectives for this Chapter 3

1.1.2 Chapter Organization 3

1.2 Foundation of Reliability Theory 4

1.2.1 Time-to-Failure Distributions 4

1.2.2 Probability and Reliability 7

1.2.3 Probability Density Function 8

1.2.4 Relationships of Distributions 12

1.2.5 Failure Rate 13

1.2.6 Expected Value and Variance 21

1.3 Failure Distributions under Extreme Stress Levels 23

1.3.1 Basic Models 23

1.3.2 Cumulative Damage Models 26

1.3.3 General Exponential Models 28

1.4 Uncertainty Measures in Parameter Estimation 30

1.5 Expected Number of Failures 34

1.5.1 Minimal Repair 35

1.5.2 Failure Replacement 37

1.5.3 Decreased Number of Failures Due to Partial Repairs 39

1.5.4 Decreased Age Due to Partial Repairs 40

1.6 System Reliability & Prognosis and Health Management 41

1.6.1 General Framework for a CBM-based PHM System 42

1.6.2 Relationship of PHM to System Reliability 44

1.6.3 Degradation Progression Signature (DPS) and Prognostics 45

1.6.4 Ideal Functional Failure Signature (FFS) and Prognostics 48

1.6.5 Non-ideal FFS and Prognostics 51

1.7 Prognostic Information 52

1.7.1 Non-ideality: Initial-Estimate Error and Remaining Useful Life (RUL) 52

1.7.2 Convergence of RUL Estimates Given an Initial Estimate Error 54

1.7.3 Prognostic Distance (PD) and Convergence 56

1.7.4 Convergence: Figure of Merit (xalpha) 56

1.7.5 Other Sources of Non-ideality in FFS Data 57

1.8 Decisions on Cost and Benefits 57

1.8.1 Product Selection 58

1.8.2 Optimal Maintenance Scheduling 61

1.8.3 Condition-based Maintenance or Replacement 65

1.8.4 Preventive Replacement Scheduling 67

1.8.5 Model Variants and Extensions 70

1.9 Introduction to PHM: Summary 72

CHAPTER 2: APPROACHES FOR PROGNOSIS AND HEALTH MANAGEMENT (PHM) 1

2.1 Approaches for Prognosis and Health Management (PHM) 1

2.1.1 Model-based Prognostic Approaches 1

2.1.2 Data-driven Prognostic Approaches 2

2.1.3 Hybrid Prognostic Approaches 2

2.1.4 Objectives for this Chapter 3

2.1.5 Chapter Organization 3

2.2 Model-based Prognostics

2.2.1 Analytical Modeling 5

2.2.2 Distribution Modeling 10

2.2.3 Physics of Failure (PoF) and Reliability Modeling 12

2.2.4 Acceleration Factor (AF) 14

2.2.5 Complexity Related to Reliability Modeling 16

2.2.6 Failure Distribution 18

2.2.7 Multiple Modes of Failure: Failure Rate and FIT 19

2.2.8 Advantages and Disadvantages of Model-based Prognostics 19

2.3 Data-driven Prognostics 20

2.3.1 Statistical Methods 20

2.3.2 Machine Learning (ML): Classification and Clustering 26

2.4 Hybrid-driven Prognostics 31

2.5 An Approach to Condition-based Maintenance (CBM) 33

2.5.1 Modeling of Condition-based Data (CBD) Signatures 33

2.5.2 Comparison of Methodologies: Life Consumption and CBD Signature 34

2.5.3 CBD-signature Modeling: An Illustration . 35

2.6 Approaches to PHM: Summary 43

CHAPTER 3 FAILURE PROGRESSION SIGNATURES 1

3.1 Introduction to Failure Signatures 1

3.2 Basic Types of Signatures 3

3.2.1 CBD Signature 3

3.2.2 FFP Signature 9

3.2.3 Transform FFP into FFS 12

3.2.4 Transform FFP into Degradation Progression Signature (DPS) 13

3.2.5 Transform DPS into DPS-based FFS 16

3.3 Model Verification 17

3.3.1 Signature Classification 17

3.3.2 Verify CBD Modeling 18

3.3.3 Verify FFP Modeling 20

3.3.4 Verify DPS Modeling 20

3.3.5 Verify DPS-based FFS Modeling 21

3.4 Evaluation of FFS Curves: Nonlinearity 22

3.4.1 Sensing System 22

3.4.2 FFS Nonlinearity 23

3.5 Summary of Data Transforms 25

3.6 Degradation Rate 29

3.6.1 Constant Degradation Rate: Linear DPS-based FFS 29

3.6.2 Non-linear Degradation Rate 30

3.7 Failure Progression Signatures and System Nodes 32

3.8 Failure Progression Signatures: Summary 33

CHAPTER 4: HEURISTIC-BASED APPROACH TO MODELING CBD SIGNATURES
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About Douglas Goodman

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.
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