Intelligent Fault Diagnosis and Prognosis for Engineering Systems gives a complete presentation of basic essentials of fault diagnosis and failure prognosis, and takes a look at the cutting-edge discipline of intelligent fault diagnosis and failure prognosis technologies for condition-based maintenance. It thoroughly details the interdisciplinary methods required to understand the physics of failure mechanisms in materials, structures, and rotating equipment, and also presents strategies to detect faults or incipient failures and predict the remaining useful life of failing components. Case studies are used throughout the book to illustrate enabling technologies.
Intelligent Fault Diagnosis and Prognosis for Engineering Systems offers material in a holistic and integrated approach that addresses the various interdisciplinary components of the field--from electrical, mechanical, industrial, and computer engineering to business management. This invaluably helpful book:
Includes state-of-the-art algorithms, methodologies, and contributions from leading experts, including cost-benefit analysis tools and performance assessment techniques
Covers theory and practice in a way that is rooted in industry research and experience
Presents the only systematic, holistic approach to a strongly interdisciplinary topic
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.
2 SYSTEMS APPROACH TO CBM/PHM.
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.
3 SENSORS AND SENSING STRATEGIES.
3.3 Sensor Placement.
3.4 Wireless Sensor Networks.
3.5 Smart Sensors.
4 SIGNAL PROCESSING AND DATABASE MANAGEMENT SYSTEMS.
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.
5 FAULT DIAGNOSIS.
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.
6 FAULT PROGNOSIS.
6.2 Model-Based Prognosis Techniques.
6.3 Probability-Based Prognosis Techniques.
6.4 Data-Driven Prediction Techniques.
6.5 Case Studies.
7 FAULT DIAGNOSIS AND PROGNOSIS PERFORMANCE METRICS.
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.
8 LOGISTICS: SUPPORT OF THE SYSTEM IN OPERATION.
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.
Frank L. Lewis The University of Texas at Arlington, Ft. Worth, TX.
Michael Roemer Michael Roemer, Impact Technologies, LLC.
Andrew Hess Naval Air Systems Command, Patuxent River, MD.
Biqing Wu Biqing Wu, Georgia Institute of Technology.