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Diagnostics and Prognostics of Engineering Systems

Book Description

Industrial Prognostics predicts an industrial system’s lifespan using probability measurements to determine the way a machine operates. Prognostics are essential in determining being able to predict and stop failures before they occur. Therefore the development of dependable prognostic procedures for engineering systems is important to increase the system’s performance and reliability. Diagnostics and Prognostics of Engineering Systems: Methods and Techniques provides widespread coverage and discussions on the methods and techniques of diagnosis and prognosis systems. Including practical examples to display the method’s effectiveness in real-world applications as well as the latest trends and research, this reference source aims to introduce fundamental theory and practice for system diagnosis and prognosis.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  5. Foreword
  6. Preface
    1. WHO AND HOW TO READ THIS BOOK
  7. Acknowledgment
  8. Section 1: Fault Tolerant Control
    1. Chapter 1: Iterative Fault Tolerant Control for General Discrete-Time Stochastic Systems Using Output Probability Density Estimation
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. ILC-BASED PDF CONTROL
      4. 3. PROBLEM FORMULATION
      5. 4. FAULT DETECTION
      6. 5. FAULT DIAGNOSIS
      7. 6. FAULT TOLERANT CONTROL
      8. 7. TUNING OF RADIAL BASIS FUNCTION
      9. 8. CONVERGENCE ANALYSIS
      10. 9. AN ILLUSTRATED EXAMPLE
      11. 10. CONCLUSION
  9. Section 2: Anomaly/Fault Detection
    1. Chapter 2: Intelligent System Monitoring
      1. ABSTRACT
      2. INTRODUCTION
      3. CONVEYOR BELT PROCESS SIMULATION
      4. ON-LINE LEARNING ADAPTIVE MODELLING
      5. DEGRADATION AUTOMATA AND SYSTEM CONDITION STATE
      6. SOLUTIONS AND RECOMMENDATIONS
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. APPENDIX
  10. Section 3: Data Driven Diagnostics
    1. Chapter 3: Principles of Classification
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. CLASSIFICATION
      5. DATA PRE-PROCESSING
      6. CONTINUOUS LEARNING
      7. CONFIDENCE OF PREDICTION
      8. GENERALIZATION
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
    2. Chapter 4: Generating Indicators for Diagnosis of Fault Levels by Integrating Information from Two or More Sensors
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. GENERATING AN INDICATOR FOR DIAGNOSIS OF FAULT LEVELS
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    3. Chapter 5: Fault Detection and Isolation for Switching Systems using a Parameter-Free Method
      1. ABSTRACT
      2. 1 INTRODUCTION
      3. 2 PROBLEM SETTING
      4. 3 SENSOR FAULT DETECTION AND ISOLATION FOR ONE MODE
      5. 4 SWITCHING TIME ESTIMATION USING ONLY ONLINE INPUT-OUTPUT DATA
      6. 5 SWITCHING TIME ESTIMATION AND CURRENT MODE RECOGNITION USING ONLINE AND OFFLINE INPUT-OUTPUT DATA
      7. 6 ALGORITHM
      8. 7 VEHICLE ROLLOVER PREVENTION EXAMPLE
      9. 8 CONCLUSION
  11. Section 4: Data Driven Prognostics
    1. Chapter 6: Data Driven Prognostics for Rotating Machinery
      1. ABSTRACT
      2. INTRODUCTION
      3. CONDITION INDICATORS FOR CONDITION MONITORING
      4. THRESHOLD SETTING AND COMPONENT HEALTH
      5. STATE SPACE MODELS FOR PROGNOSTICS
      6. CONCLUSION
  12. Section 5: Degradation Modeling
    1. Chapter 7: Identifying Suitable Degradation Parameters for Individual-Based Prognostics
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. METHODOLOGY
      5. APPLICATION AND RESULTS
      6. CONCLUSION
      7. ACRONYMS AND ABBREVIATIONS
    2. Chapter 8: Modeling Multi-State Equipment Degradation with Non-Homogeneous Continuous-Time Hidden Semi-Markov Process
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MULTI-STATE DEGRADATION MODELING WITH MARKOV RENEWAL PROCESS
      5. ESTIMATION OF NHCTHSMP PARAMETERS FOR MULTI-STATE EQUIPMENT
      6. NUMERICAL EXAMPLE
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
    3. Chapter 9: Stochastic Fatigue of a Mechanical System Using Random Transformation Technique
      1. ABSTRACT
      2. INTRODUCTION
      3. CONCLUSION
    4. Chapter 10: Degradation Based Condition Classification and Prediction in Rotating Machinery Prognostics
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ANOMALY DEFINITION AND DEGRADATION DETECTION
      5. DEGRADATION BASED CONDITION CLASSIFICATION
      6. DEGRADATION BASED CONDITION PREDICTION
      7. CASE OF CONDITION CLASSIFICATION AND PREDICTION IN ROTATING MACHINERY
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
  13. Section 6: Diagnostics
    1. Chapter 11: A Temporal Probabilistic Approach for Continuous Tool Condition Monitoring
      1. ABSTRACT
      2. INTRODUCTION
      3. HIDDEN SEMI-MARKOV MODEL-BASED APPROACH
      4. COMPUTATIONALLY SIMPLIFIED FORWARD-BACKWARD ALGORITHM
      5. DIAGNOSTICS AND PROGNOSTICS
      6. DATASET AND FEATURES
      7. DIAGNOSTICS AND PROGNOSTICS RESULTS
      8. CONCLUSION
  14. Section 7: Integration of Control and Prognostics
    1. Chapter 12: Combining Health Monitoring and Control
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. CONTROL AND PROGNOSIS INTEGRATION
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. ACRONYMS USED
  15. Section 8: Integrated Prognostics
    1. Chapter 13: A Particle Filtering Based Approach for Gear Prognostics
      1. ABSTRACT
      2. INTRODUCTION
      3. THE APPROACH
      4. SPIRAL BEVEL GEAR CASE STUDY
      5. SUMMARY
  16. Section 9: Life Cycle Cost and Return on Investment for Prognostics and Health Management
    1. Chapter 14: Supporting Business Cases for PHM
      1. ABSTRACT
      2. INTRODUCTION
      3. RETURN ON INVESTMENT (ROI)
      4. SYSTEM-LEVEL MAINTENANCE VALUE
      5. AVAILABILITY REQUIREMENTS
      6. CONCLUSION
  17. Section 10: Physics Based Diagnostics
    1. Chapter 15: Remote Fault Diagnosis System for Marine Power Machinery System
      1. ABSTRACT
      2. INTRODUCTION
      3. DESCRIPTION OF THE KNOWLEDGE-BASED REMOTE DIAGNOSTIC SYSTEM
      4. CASE STUDY: GEAR PUMP DAMAGE DETECTION AND DIAGNOSIS
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
  18. Section 11: Prognostics
    1. Chapter 16: Prognostics and Health Management of Choke Valves Subject to Erosion
      1. ABSTRACT
      2. INTRODUCTION
      3. 2 GENERAL GUIDELINES AND STANDARDS FOR CBM
      4. 3 CONDITION-BASED MAINTENANCE OF CHOKE VALVES
      5. 4 CHOKE VALVE EROSION: THE CASE STUDY
      6. 5 CHOKE VALVE CONDITION MONITORING
      7. 6. CHOKE VALVE REMAINING USEFUL LIFE ESTIMATION
      8. 7 CONCLUSION
  19. Section 12: Prognostics (Review)
    1. Chapter 17: Prognostics and Health Management of Industrial Equipment
      1. ABSTRACT
      2. INTRODUCTION
      3. CHARACTERISTICS OF PROGNOSTICS METHODS
      4. INFORMATION AND DATA FOR PHM
      5. APPROACHES TO PHM
      6. EXAMPLES
      7. CHALLENGES AND WAYS FORWARD
      8. SUMMARY AND CONCLUSION
  20. Section 13: Prognostics (Structure)
    1. Chapter 18: Structure Reliability and Response Prognostics under Uncertainty Using Bayesian Analysis and Analytical Approximations
      1. ABSTRACT
      2. INTRODUCTION
      3. BAYESIAN MODELING AND LAPLACE APPROXIMATION
      4. RELIABILITY ESTIMATE USING FIRST- AND SECOND-ORDER RELIABILITY METHOD
      5. SYSTEM RESPONSE ESTIMATE USING INVERSE FORM METHOD
      6. EXAMPLES
      7. CONCLUSION
    2. Chapter 19: Fatigue Damage Prognostics and Life Prediction with Dynamic Response Reconstruction Using Indirect Sensor Measurements
      1. ABSTRACT
      2. INTRODUCTION
      3. EMPIRICAL MODE DECOMPOSITION
      4. TIME-DERIVATIVE FATIGUE CRACK GROWTH MODEL
      5. EXAMPLES
      6. CONCLUSION
  21. Compilation of References
  22. About the Contributors