{"product_id":"intelligent-fault-diagnosis-and-prognosis-for-engineering-systems-isbn-9780471729990","title":"Intelligent Fault Diagnosis and Prognosis for Engineering Systems","description":"Expert guidance on theory and practice in condition-based intelligent machine fault diagnosis and failure prognosis\u003cbr\u003e \u003cbr\u003e \u003ci\u003eIntelligent Fault Diagnosis and Prognosis for Engineering Systems\u003c\/i\u003e 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.\u003cbr\u003e \u003cbr\u003e 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:\u003cbr\u003e * Includes state-of-the-art algorithms, methodologies, and contributions from leading experts, including cost-benefit analysis tools and performance assessment techniques\u003cbr\u003e * Covers theory and practice in a way that is rooted in industry research and experience\u003cbr\u003e * Presents the only systematic, holistic approach to a strongly interdisciplinary topic  PREFACE.  \u003cp\u003eACKNOWLEDGMENTS.\u003c\/p\u003e \u003cp\u003ePROLOGUE.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 INTRODUCTION.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Historical Perspective.\u003c\/p\u003e \u003cp\u003e1.2 Diagnostic and Prognostic System Requirements.\u003c\/p\u003e \u003cp\u003e1.3 Designing in Fault Diagnostic and Prognostic Systems.\u003c\/p\u003e \u003cp\u003e1.4 Diagnostic and Prognostic Functional Layers.\u003c\/p\u003e \u003cp\u003e1.5 Preface to Book Chapters.\u003c\/p\u003e \u003cp\u003e1.6 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 SYSTEMS APPROACH TO CBM\/PHM.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction.\u003c\/p\u003e \u003cp\u003e2.2 Trade Studies.\u003c\/p\u003e \u003cp\u003e2.3 Failure Modes and Effects Criticality Analysis (FMECA).\u003c\/p\u003e \u003cp\u003e2.4 System CBM Test-Plan Design.\u003c\/p\u003e \u003cp\u003e2.5 Performance Assessment.\u003c\/p\u003e \u003cp\u003e2.6 CBM\/PHM Impact on Maintenance and Operations: Case Studies.\u003c\/p\u003e \u003cp\u003e2.7 CBM\/PHM in Control and Contingency Management.\u003c\/p\u003e \u003cp\u003e2.8 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 SENSORS AND SENSING STRATEGIES.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 Sensors.\u003c\/p\u003e \u003cp\u003e3.3 Sensor Placement.\u003c\/p\u003e \u003cp\u003e3.4 Wireless Sensor Networks.\u003c\/p\u003e \u003cp\u003e3.5 Smart Sensors.\u003c\/p\u003e \u003cp\u003e3.6 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 SIGNAL PROCESSING AND DATABASE MANAGEMENT SYSTEMS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 Signal Processing in CBM\/PHM.\u003c\/p\u003e \u003cp\u003e4.3 Signal Preprocessing.\u003c\/p\u003e \u003cp\u003e4.4 Signal Processing.\u003c\/p\u003e \u003cp\u003e4.5 Vibration Monitoring and Data Analysis.\u003c\/p\u003e \u003cp\u003e4.6 Real-Time Image Feature Extraction and Defect\/Fault Classification.\u003c\/p\u003e \u003cp\u003e4.7 The Virtual Sensor.\u003c\/p\u003e \u003cp\u003e4.8 Fusion or Integration Technologies.\u003c\/p\u003e \u003cp\u003e4.9 Usage-Pattern Tracking.\u003c\/p\u003e \u003cp\u003e4.10 Database Management Methods.\u003c\/p\u003e \u003cp\u003e4.11 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 FAULT DIAGNOSIS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 The Diagnostic Framework.\u003c\/p\u003e \u003cp\u003e5.3 Historical Data Diagnostic Methods.\u003c\/p\u003e \u003cp\u003e5.4 Data-Driven Fault Classification and Decision Making.\u003c\/p\u003e \u003cp\u003e5.5 Dynamic Systems Modeling.\u003c\/p\u003e \u003cp\u003e5.6 Physical Model–Based Methods.\u003c\/p\u003e \u003cp\u003e5.7 Model-Based Reasoning.\u003c\/p\u003e \u003cp\u003e5.8 Case-Based Reasoning (CBR).\u003c\/p\u003e \u003cp\u003e5.9 Other Methods for Fault Diagnosis.\u003c\/p\u003e \u003cp\u003e5.10 A Diagnostic Framework for Electrical\/Electronic Systems.\u003c\/p\u003e \u003cp\u003e5.11 Case Study: Vibration-Based Fault Detection and Diagnosis for Engine Bearings.\u003c\/p\u003e \u003cp\u003e5.12 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 FAULT PROGNOSIS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 Model-Based Prognosis Techniques.\u003c\/p\u003e \u003cp\u003e6.3 Probability-Based Prognosis Techniques.\u003c\/p\u003e \u003cp\u003e6.4 Data-Driven Prediction Techniques.\u003c\/p\u003e \u003cp\u003e6.5 Case Studies.\u003c\/p\u003e \u003cp\u003e6.6 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 FAULT DIAGNOSIS AND PROGNOSIS PERFORMANCE METRICS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 CBM\/PHM Requirements Definition.\u003c\/p\u003e \u003cp\u003e7.3 Feature-Evaluation Metrics.\u003c\/p\u003e \u003cp\u003e7.4 Fault Diagnosis Performance Metrics.\u003c\/p\u003e \u003cp\u003e7.5 Prognosis Performance Metrics.\u003c\/p\u003e \u003cp\u003e7.6 Diagnosis and Prognosis Effectiveness Metrics.\u003c\/p\u003e \u003cp\u003e7.7 Complexity\/Cost-Benefit Analysis of CBM\/PHM Systems.\u003c\/p\u003e \u003cp\u003e7.8 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 LOGISTICS: SUPPORT OF THE SYSTEM IN OPERATION.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Product-Support Architecture, Knowledge Base, and Methods for CBM.\u003c\/p\u003e \u003cp\u003e8.3 Product Support without CBM.\u003c\/p\u003e \u003cp\u003e8.4 Product Support with CBM.\u003c\/p\u003e \u003cp\u003e8.5 Maintenance Scheduling Strategies.\u003c\/p\u003e \u003cp\u003e8.6 A Simple Example.\u003c\/p\u003e \u003cp\u003e8.7 References.\u003c\/p\u003e \u003cp\u003eAPPENDIX.\u003c\/p\u003e \u003cp\u003eINDEX.\u003c\/p\u003e  \u003cb\u003eGeorge Vachtsevanos\u003c\/b\u003e, 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.  \u003cp\u003e\u003cb\u003eFrank L. Lewis\u003c\/b\u003e, 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMichael Roemer\u003c\/b\u003e, Phd, is Director of Engineering at Impact Technologies, LLC, in Rochester, New York.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAndrew Hess\u003c\/b\u003e is Air System PHM Lead and Development Manager in the Joint Strike Fighter Program Office at Naval Air Systems Command, in Patuxent River, Maryland.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBiqing Wu\u003c\/b\u003e, 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.\u003c\/p\u003e  \u003cb\u003eExpert guidance on theory and practice in condition-based intelligent machine fault diagnosis and failure prognosis\u003c\/b\u003e  \u003cp\u003e\u003ci\u003eIntelligent Fault Diagnosis and Prognosis for Engineering Systems\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eIntelligent Fault Diagnosis and Prognosis for Engineering Systems\u003c\/i\u003e 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eIncludes state-of-the-art algorithms, methodologies, and contributions from leading experts, including cost-benefit analysis tools and performance assessment techniques\u003c\/li\u003e \u003cli\u003eCovers theory and practice in a way that is rooted in industry research and experience\u003c\/li\u003e \u003cli\u003ePresents the only systematic, holistic approach to a strongly interdisciplinary topic\u003c\/li\u003e \u003c\/ul\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989440610533,"sku":"NP9780471729990","price":195.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780471729990.jpg?v=1761784107","url":"https:\/\/k12savings.com\/es\/products\/intelligent-fault-diagnosis-and-prognosis-for-engineering-systems-isbn-9780471729990","provider":"K12savings","version":"1.0","type":"link"}