Intelligent Fault Diagnosis And Prognosis For Engineering Systems / Edition 1 available in Hardcover
- Pub. Date:
Expert guidance on theory and practice in condition-basedintelligent machine fault diagnosis and failure prognosisIntelligent Fault Diagnosis and Prognosis for EngineeringSystems gives a complete presentation of basic essentials offault diagnosis and failure prognosis, and takes a look at thecutting-edge discipline of intelligent fault diagnosis and failureprognosis technologies for condition-based maintenance. Itthoroughly details the interdisciplinary methods required tounderstand the physics of failure mechanisms in materials,structures, and rotating equipment, and also presents strategies todetect faults or incipient failures and predict the remaininguseful life of failing components. Case studies are used throughoutthe book to illustrate enabling technologies.Intelligent Fault Diagnosis and Prognosis for Engineering Systemsoffers material in a holistic and integrated approach thataddresses the various interdisciplinary components of thefieldfrom electrical, mechanical, industrial, and computerengineering to business management. This invaluably helpfulbook:* Includes state-of-the-art algorithms, methodologies, andcontributions from leading experts, including cost-benefit analysistools and performance assessment techniques* Covers theory and practice in a way that is rooted in industryresearch and experience* Presents the only systematic, holistic approach to a stronglyinterdisciplinary topic
|Product dimensions:||6.44(w) x 9.37(h) x 1.43(d)|
About the Author
George Vachtsevanos, Phd, is Director of the IntelligentControl Systems Laboratory in the School of Electrical and ComputerEngineering 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 ResearchInstitute at The University of Texas at Arlington, in Fort Worth,Texas.
Michael Roemer, Phd, is Director of Engineering at ImpactTechnologies, LLC, in Rochester, New York.
Andrew Hess is Air System PHM Lead and DevelopmentManager in the Joint Strike Fighter Program Office at Naval AirSystems Command, in Patuxent River, Maryland.
Biqing Wu, Phd, works on various topics of activedisturbance control and CBM/PHM. She is currently serving as aresearch engineer at the Georgia Institute of Technology, inAtlanta, Georgia.
Table of Contents
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: CaseStudies.
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/FaultClassification.
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/ElectronicSystems.
5.11 Case Study: Vibration-Based Fault Detection and Diagnosisfor 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 Methodsfor CBM.
8.3 Product Support without CBM.
8.4 Product Support with CBM.
8.5 Maintenance Scheduling Strategies.
8.6 A Simple Example.