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Machine Fault Diagnosis and Prognosis presents the basic foundations for fault diagnosis and prognosis and the newly emerging discipline of 'intelligent maintenance and condition-based diagnosis.' It details the methods required to understand the physics of failure mechanisms in materials, structures, and rotating equipment, and it also presents strategies to detect faults or incipient failures and predict the remaining useful life of failing components.
INDICE: Preface and Acknowledgements. Prologue. Chapter 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. Chapter 2. The 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.. Chapter 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. Chapter 4. Signal Processing/Data Base Management. 4.1 Introduction. 4.2 Signal Processing in CBM/PHM. 4.3 Signal Pre-processing. 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 References. Chapter 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 Dynamical 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.. Chapter 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. Chapter 7. Fault Diagnosis and Prognosis Performance Metrics. 7.1 Introduction. 7.2 PHM/CBM Requirement 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. Chapter 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.
Reliability and systems engineers in industry especially automotive, aerospace, electronics and computer industries. Members of the American Society for Quality (ASQ), the Institute of Electrical and Electronics Engineers (IEEE) Reliability Society, and the Society of Automotive Engineers. Graduate students in machine diagnostics and quality/reliability engineering courses in departments of mechanical, aerospace, electrical, and industrial engineering.
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