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Prognostics and Health Management

Author Ferenc Szidarovszky , James P. Hofmeister , Douglas Goodman
Release Date: 2019/06/01
ISBN: 9781119356653
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Book Description

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.

Table of Contents

  1. Cover
  2. List of Figures
  3. Series Editor's Foreword
  4. Preface
  5. Acknowledgments
  6. 1 Introduction to Prognostics
    1. 1.1 What Is Prognostics?
    2. 1.2 Foundation of Reliability Theory
    3. 1.3 Failure Distributions Under Extreme Stress Levels
    4. 1.4 Uncertainty Measures in Parameter Estimation
    5. 1.5 Expected Number of Failures
    6. 1.6 System Reliability and Prognosis and Health Management
    7. 1.7 Prognostic Information
    8. 1.8 Decisions on Cost and Benefits
    9. 1.9 Introduction to PHM: Summary
    10. References
    11. Further Reading
  7. 2 Approaches for Prognosis and Health Management/Monitoring (PHM)
    1. 2.1 Introduction to Approaches for Prognosis and Health Management/Monitoring (PHM)
    2. 2.2 Model‐Based Prognostics
    3. 2.3 Data‐Driven Prognostics
    4. 2.4 Hybrid‐Driven Prognostics
    5. 2.5 An Approach to Condition‐Based Maintenance (CBM)
    6. 2.6 Approaches to PHM: Summary
    7. References
    8. Further Reading
  8. 3 Failure Progression Signatures
    1. 3.1 Introduction to Failure Signatures
    2. 3.2 Basic Types of Signatures
    3. 3.3 Model Verification
    4. 3.4 Evaluation of FFS Curves: Nonlinearity
    5. 3.5 Summary of Data Transforms
    6. 3.6 Degradation Rate
    7. 3.7 Failure Progression Signatures and System Nodes
    8. 3.8 Failure Progression Signatures: Summary
    9. References
    10. Further Reading
  9. 4 Heuristic‐Based Approach to Modeling CBD Signatures
    1. 4.1 Introduction to Heuristic‐Based Modeling of Signatures
    2. 4.2 General Modeling Considerations: CBD Signatures
    3. 4.3 CBD Modeling: Degradation‐Signature Models
    4. 4.4 DPS Modeling: FFP to DPS Transform Models
    5. 4.5 FFS Modeling: Failure Level and Signature Modeling
    6. 4.6 Heuristic‐Based Approach to Modeling of Signatures: Summary
    7. References
    8. Further Reading
  10. 5 Non‐Ideal Data: Effects and Conditioning
    1. 5.1 Introduction to Non‐Ideal Data: Effects and Conditioning
    2. 5.2 Heuristic‐Based Approach Applied to Non‐Ideal CBD Signatures
    3. 5.3 Errors and Non‐Ideality in FFS Data
    4. 5.4 Heuristic Method for Adjusting FFS Data
    5. 5.5 Summary: Non‐Ideal Data, Effects, and Conditioning
    6. References
    7. Further Reading
  11. 6 Design: Robust Prototype of an Exemplary PHM System
    1. 6.1 PHM System: Review
    2. 6.2 Design Approaches for a PHM System
    3. 6.3 Sampling and Polling
    4. 6.4 Initial Design Specifications
    5. 6.5 Special RMS Method for AC Phase Currents
    6. 6.6 Diagnostic and Prognostic Procedure
    7. 6.7 Specifications: Robustness and Capability
    8. 6.8 Node Specifications
    9. 6.9 System Verification and Performance Metrics
    10. 6.10 System Verification: Advanced Prognostics
    11. 6.11 PHM System Verification: EMA Faults
    12. 6.12 PHM System Verification: Functional Integration
    13. 6.13 Summary: A Robust Prototype PHM System
    14. References
    15. Further Reading
  12. 7 Prognostic Enabling: Selection, Evaluation, and Other Considerations
    1. 7.1 Introduction to Prognostic Enabling
    2. 7.2 Prognostic Targets: Evaluation, Selection, and Specifications
    3. 7.3 Example: Cost‐Benefit of Prognostic Approaches
    4. 7.4 Reliability: Bathtub Curve
    5. 7.5 Chapter Summary and Book Conclusion
    6. References
    7. Further Reading
  13. Index
  14. End User License Agreement
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