Prognostics and Health Management : A Practical Approach to Improving System Reliability Using Condition-Based Data.
- 1st ed.
- 1 online resource (385 pages)
- Quality and Reliability Engineering Series .
- Quality and Reliability Engineering Series .
Cover -- Title Page -- Copyright -- Contents -- List of Figures -- Series Editor's Foreword -- Preface -- Acknowledgments -- Chapter 1 Introduction to Prognostics -- 1.1 What Is Prognostics? -- 1.1.1 Chapter Objectives -- 1.1.2 Chapter Organization -- 1.2 Foundation of Reliability Theory -- 1.2.1 Time‐to‐Failure Distributions -- 1.2.2 Probability and Reliability -- 1.2.3 Probability Density Function -- 1.2.4 Relationships of Distributions -- 1.2.5 Failure Rate -- 1.2.6 Expected Value and Variance -- 1.3 Failure Distributions Under Extreme Stress Levels -- 1.3.1 Basic Models -- 1.3.2 Cumulative Damage Models -- 1.3.3 General Exponential Models -- 1.4 Uncertainty Measures in Parameter Estimation -- 1.5 Expected Number of Failures -- 1.5.1 Minimal Repair -- 1.5.2 Failure Replacement -- 1.5.3 Decreased Number of Failures Due to Partial Repairs -- 1.5.4 Decreased Age Due to Partial Repairs -- 1.6 System Reliability and Prognosis and Health Management -- 1.6.1 General Framework for a CBM‐Based PHM System -- 1.6.2 Relationship of PHM to System Reliability -- 1.6.3 Degradation Progression Signature (DPS) and Prognostics -- 1.6.4 Ideal Functional Failure Signature (FFS) and Prognostics -- 1.6.5 Non‐ideal FFS and Prognostics -- 1.7 Prognostic Information -- 1.7.1 Non‐ideality: Initial‐Estimate Error and Remaining Useful Life (RUL) -- 1.7.2 Convergence of RUL Estimates Given an Initial Estimate Error -- 1.7.3 Prognostic Distance (PD) and Convergence -- 1.7.4 Convergence: Figure of Merit (χα) -- 1.7.5 Other Sources of Non‐ideality in FFS Data -- 1.8 Decisions on Cost and Benefits -- 1.8.1 Product Selection -- 1.8.2 Optimal Maintenance Scheduling -- 1.8.3 Condition‐Based Maintenance or Replacement -- 1.8.4 Preventive Replacement Scheduling -- 1.8.5 Model Variants and Extensions -- 1.9 Introduction to PHM: Summary -- References -- Further Reading. Chapter 2 Approaches for Prognosis and Health Management/Monitoring (PHM) -- 2.1 Introduction to Approaches for Prognosis and Health Management/Monitoring (PHM) -- 2.1.1 Model‐Based Prognostic Approaches -- 2.1.2 Data‐Driven Prognostic Approaches -- 2.1.3 Hybrid Prognostic Approaches -- 2.1.4 Chapter Objectives -- 2.1.5 Chapter Organization -- 2.2 Model‐Based Prognostics -- 2.2.1 Analytical Modeling -- 2.2.2 Distribution Modeling -- 2.2.3 Physics of Failure (PoF) and Reliability Modeling -- 2.2.4 Acceleration Factor (AF) -- 2.2.5 Complexity Related to Reliability Modeling -- 2.2.6 Failure Distribution -- 2.2.7 Multiple Modes of Failure: Failure Rate and FIT -- 2.2.8 Advantages and Disadvantages of Model‐Based Prognostics -- 2.3 Data‐Driven Prognostics -- 2.3.1 Statistical Methods -- 2.3.2 Machine Learning (ML): Classification and Clustering -- 2.4 Hybrid‐Driven Prognostics -- 2.5 An Approach to Condition‐Based Maintenance (CBM) -- 2.5.1 Modeling of Condition‐Based Data (CBD) Signatures -- 2.5.2 Comparison of Methodologies: Life Consumption and CBD Signature -- 2.5.3 CBD‐Signature Modeling: An Illustration -- 2.6 Approaches to PHM: Summary -- References -- Further Reading -- Chapter 3 Failure Progression Signatures -- 3.1 Introduction to Failure Signatures -- 3.1.1 Chapter Objectives -- 3.1.2 Chapter Organization -- 3.2 Basic Types of Signatures -- 3.2.1 CBD Signature -- 3.2.2 FFP Signature -- 3.2.3 Transforming FFP into FFS -- 3.2.4 Transforming FFP into a Degradation Progression Signature (DPS) -- 3.2.5 Transforming DPS into DPS‐Based FFS -- 3.3 Model Verification -- 3.3.1 Signature Classification -- 3.3.2 Verifying CBD Modeling -- 3.3.3 Verifying FFP Modeling -- 3.3.4 Verifying DPS Modeling -- 3.3.5 Verifying DPS‐Based FFS Modeling -- 3.4 Evaluation of FFS Curves: Nonlinearity -- 3.4.1 Sensing System -- 3.4.2 FFS Nonlinearity. 3.5 Summary of Data Transforms -- 3.6 Degradation Rate -- 3.6.1 Constant Degradation Rate: Linear DPS‐Based FFS -- 3.6.2 Nonlinear Degradation Rate -- 3.7 Failure Progression Signatures and System Nodes -- 3.8 Failure Progression Signatures: Summary -- References -- Further Reading -- Chapter 4 Heuristic‐Based Approach to Modeling CBD Signatures -- 4.1 Introduction to Heuristic‐Based Modeling of Signatures -- 4.1.1 Review of Chapter 3 -- 4.1.2 Theory: Heuristic Modeling of CBD Signatures -- 4.1.3 Chapter Objectives -- 4.1.4 Chapter Organization -- 4.2 General Modeling Considerations: CBD Signatures -- 4.2.1 Noise Margin -- 4.2.2 Definition of a Degradation‐Signature Model -- 4.2.3 Feature Data: Nominal Value -- 4.2.4 Feature Data, Fault‐to‐Failure Progression Signature, and Degradation‐Signature Model -- 4.2.5 Approach to Transforming CBD Signatures into FFS Data -- 4.3 CBD Modeling: Degradation‐Signature Models -- 4.3.1 Representative Examples: Degradation‐Signature Models -- 4.3.2 Example Plots of Representative FFP Degradation Signatures -- 4.3.3 Converting Decreasing Signatures to Increasing Signatures -- 4.4 DPS Modeling: FFP to DPS Transform Models -- 4.4.1 Developing Transform Models: FFP to DPS -- 4.4.2 Example Plots of FFP Signatures and DPS Signatures -- 4.5 FFS Modeling: Failure Level and Signature Modeling -- 4.5.1 Developing DPS‐Based Failure Level (FL) Models Using FFP Defined Failure Levels -- 4.5.2 Modeling Results for Failure Levels: FFP‐Based and DPS‐Based -- 4.5.3 Transforming DPS Data into FFS Data -- 4.6 Heuristic‐Based Approach to Modeling of Signatures: Summary -- References -- Further Reading -- Chapter 5 Non‐Ideal Data: Effects and Conditioning -- 5.1 Introduction to Non‐Ideal Data: Effects and Conditioning -- 5.1.1 Review of Chapter 4 -- 5.1.2 Data Acquisition, Manipulation, and Transformation -- 5.1.3 Chapter Objectives. 5.1.4 Chapter Organization -- 5.2 Heuristic‐Based Approach Applied to Non‐Ideal CBD Signatures -- 5.2.1 Summary of a Heuristic‐Based Approach Applied to Non‐Ideal CBD Signatures -- 5.2.2 Example Target for Prognostic Enabling -- 5.2.3 Noise is an Issue in Achieving High Accuracy in Prognostic Information -- 5.3 Errors and Non‐Ideality in FFS Data -- 5.3.1 Noise Margin and Offset Errors -- 5.3.2 Measurement Error, Uncertainty, and Sampling -- 5.3.3 Other Sources of Noise -- 5.3.4 Data Smoothing and Non‐Ideality in FFS Data -- 5.4 Heuristic Method for Adjusting FFS Data -- 5.4.1 Description of a Method for Adjusting FFS Data -- 5.4.2 Adjusted FFS Data -- 5.4.3 Data Conditioning: Another Example Data Set -- 5.5 Summary: Non‐Ideal Data, Effects, and Conditioning -- References -- Further Reading -- Chapter 6 Design: Robust Prototype of an Exemplary PHM System -- 6.1 PHM System: Review -- 6.1.1 Chapter 1: Introduction to Prognostics -- 6.1.2 Chapter 2: Prognostic Approaches for Prognosis and Health Management -- 6.1.3 Chapter 3: Failure Progression Signatures -- 6.1.4 Chapter 4: Heuristic‐Based Approach to Modeling CBD Signatures -- 6.1.5 Chapter 5: Non‐Ideal Data: Effects and Conditioning -- 6.1.6 Chapter Objectives -- 6.1.7 Chapter Organization -- 6.2 Design Approaches for a PHM System -- 6.2.1 Selecting and Evaluating Targets and Their Failure Modes -- 6.2.2 Offline Prognostic Approaches: Selecting Results -- 6.2.3 Selecting a Base Architecture for the Online Phase -- 6.3 Sampling and Polling -- 6.3.1 Continual - Periodic Sampling -- 6.3.2 Periodic‐Burst Sampling -- 6.3.3 Polling -- 6.4 Initial Design Specifications -- 6.4.1 Operation: Test/Demonstration vs. Real -- 6.4.2 Test Bed -- 6.4.3 Test Bed: Results -- 6.5 Special RMS Method for AC Phase Currents -- 6.5.1 Peak‐RMS Method -- 6.5.2 Special Peak‐RMS Method: Base Computational Routine. 6.5.3 Special Peak‐RMS Method: FFP Computational Routine -- 6.5.4 Peak‐RMS Method: EMA -- 6.6 Diagnostic and Prognostic Procedure -- 6.6.1 SMPS Power Supply -- 6.6.2 EMA -- 6.7 Specifications: Robustness and Capability -- 6.7.1 Node‐Based Architecture -- 6.7.2 Example Design -- 6.8 Node Specifications -- 6.8.1 System Node Definition -- 6.8.2 Node Definition -- 6.8.3 Other Node Definitions for the Prototype PHM System -- 6.9 System Verification and Performance Metrics -- 6.9.1 Offset Types of Errors -- 6.9.2 Uncertainty in Determining Prognostic Distance -- 6.9.3 Estimating Convergence to Within PHα -- 6.9.4 Performance Metrics -- 6.9.5 Prognostic Information: RUL, SoH, PH, and Degradation -- 6.10 System Verification: Advanced Prognostics -- 6.10.1 SMPS: FFP Signature Directly to FFS -- 6.10.2 SMPS: FFP Signature to DPS to FFS -- 6.11 PHM System Verification: EMA Faults -- 6.11.1 EMA: Load (Friction) Type of Fault -- 6.11.2 EMA: Winding Type of Fault -- 6.11.3 EMA: Power‐Switching Transistor Type of Fault -- 6.12 PHM System Verification: Functional Integration -- 6.12.1 Functional Integration: Control and Data Flow -- 6.12.2 System Performance Metrics: Summary -- 6.12.3 PHM System: Plans -- 6.13 Summary: A Robust Prototype PHM System -- References -- Further Reading -- Chapter 7 Prognostic Enabling: Selection, Evaluation, and Other Considerations -- 7.1 Introduction to Prognostic Enabling -- 7.1.1 Review of Chapter 6 -- 7.1.2 Electronic Health Solutions -- 7.1.3 Critical Systems and Advance Warning -- 7.1.4 Reduction in Maintenance -- 7.1.5 Health Management, Maintenance, and Logistics -- 7.1.6 Chapter Objectives -- 7.1.7 Chapter Organization -- 7.2 Prognostic Targets: Evaluation, Selection, and Specifications -- 7.2.1 Criteria for Evaluation, Selection, and Winnowing -- 7.2.2 Meaning of MTBF and MTTF -- 7.2.3 MTTF and MTBF Uncertainty. 7.2.4 TTF and PITTFF.
9781119356691
Machinery-Reliability. Equipment health monitoring. Machinery-Maintenance and repair-Planning. Structural failures-Mathematical models.