Research and Markets (http://www.researchandmarkets.com/reports/c42940) has announced the addition of Intelligent Fault Diagnosis and Prognosis for Engineering Systems: Methods and Case Studies to their offering.
Expert guidance on theory and practice in condition-based intelligent machine fault diagnosis and failure prognosis.
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.
About the authors
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 and Acknowledgements.
Prologue.
The Chapters contained inside this report include:
Chapter 1. Introduction.
Chapter 2. The Systems Approach to CBM/PHM.
Chapter 3. Sensors and Sensing Strategies.
Chapter 4. Signal Processing/Data Base Management.
Chapter 5. Fault Diagnosis.
Chapter 6. Fault Prognosis.
Chapter 7. Fault Diagnosis and Prognosis Performance Metrics.
Chapter 8. Logistics: Support of the System in Operation.
Appendix
This book presents the basic foundations for fault diagnosis and prognosis and the newly emerging discipline of "intelligent maintenance and condition-based diagnosis." This interdisciplinary field requires components from electrical, mechanical, industrial, and computer engineering, as well as business management. The book details those methods required to understand the physics of failure mechanisms in materials, structures, and rotating equipment; it presents strategies to detect faults or incipient failures and predict the remaining useful life of failing components. Content is presented using a holistic and integrated approach to Condition Based Maintenance including cost-benefit analysis tools, and performance assessment techniques.
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.
For more information visit http://www.researchandmarkets.com/reports/c42940.
