Intelligent Fault Diagnosis and Prognosis for Engineering Systems

George Vachtsevanos, Phd, is Director of the Intelligent Control Systems Laboratory in the School of Electrical and Computer Engineering at Georgia Institute of Technology, in Atlanta, Georgia. Frank L. Lewis, Phd, is Head of the Advanced Controls, Sensors, and MEMS Group in the Automation and Robotics Research Institute at The University of Texas at Arlington, in Fort Worth, Texas.Michael Roemer, Phd, is Director of Engineering at Impact Technologies, LLC, in Rochester, New York.Andrew Hess is Air System PHM Lead and Development Manager in the Joint Strike Fighter Program Office at Naval Air Systems Command, in Patuxent River, Maryland.Biqing Wu, Phd, works on various topics of active disturbance control and CBM/PHM. She is currently serving as a research engineer at the Georgia Institute of Technology, in Atlanta, Georgia.

• PREFACE. ACKNOWLEDGMENTS.PROLOGUE.1 INTRODUCTION.1.1 Historical Perspective.1.2 Diagnostic and Prognostic System Requirements.1.3 Designing in Fault Diagnostic and Prognostic Systems.1.4 Diagnostic and Prognostic Functional Layers.1.5 Preface to Book Chapters.1.6 References.2 SYSTEMS APPROACH TO CBM/PHM.2.1 Introduction.2.2 Trade Studies.2.3 Failure Modes and Effects Criticality Analysis (FMECA).2.4 System CBM Test-Plan Design.2.5 Performance Assessment.2.6 CBM/PHM Impact on Maintenance and Operations: Case Studies.2.7 CBM/PHM in Control and Contingency Management.2.8 References.3 SENSORS AND SENSING STRATEGIES.3.1 Introduction.3.2 Sensors.3.3 Sensor Placement.3.4 Wireless Sensor Networks.3.5 Smart Sensors.3.6 References.4 SIGNAL PROCESSING AND DATABASE MANAGEMENT SYSTEMS.4.1 Introduction.4.2 Signal Processing in CBM/PHM.4.3 Signal Preprocessing.4.4 Signal Processing.4.5 Vibration Monitoring and Data Analysis.4.6 Real-Time Image Feature Extraction and Defect/Fault Classification.4.7 The Virtual Sensor.4.8 Fusion or Integration Technologies.4.9 Usage-Pattern Tracking.4.10 Database Management Methods.4.11 References.5 FAULT DIAGNOSIS.5.1 Introduction.5.2 The Diagnostic Framework.5.3 Historical Data Diagnostic Methods.5.4 Data-Driven Fault Classification and Decision Making.5.5 Dynamic Systems Modeling.5.6 Physical Model–Based Methods.5.7 Model-Based Reasoning.5.8 Case-Based Reasoning (CBR).5.9 Other Methods for Fault Diagnosis.5.10 A Diagnostic Framework for Electrical/Electronic Systems.5.11 Case Study: Vibration-Based Fault Detection and Diagnosis for Engine Bearings.5.12 References.6 FAULT PROGNOSIS.6.1 Introduction.6.2 Model-Based Prognosis Techniques.6.3 Probability-Based Prognosis Techniques.6.4 Data-Driven Prediction Techniques.6.5 Case Studies.6.6 References.7 FAULT DIAGNOSIS AND PROGNOSIS PERFORMANCE METRICS.7.1 Introduction.7.2 CBM/PHM Requirements Definition.7.3 Feature-Evaluation Metrics.7.4 Fault Diagnosis Performance Metrics.7.5 Prognosis Performance Metrics.7.6 Diagnosis and Prognosis Effectiveness Metrics.7.7 Complexity/Cost-Benefit Analysis of CBM/PHM Systems.7.8 References.8 LOGISTICS: SUPPORT OF THE SYSTEM IN OPERATION.8.1 Introduction.8.2 Product-Support Architecture, Knowledge Base, and Methods for CBM.8.3 Product Support without CBM.8.4 Product Support with CBM.8.5 Maintenance Scheduling Strategies.8.6 A Simple Example.8.7 References.APPENDIX.INDEX.