Structural Health Monitoring
Language: English
Published by John Wiley & Sons Jan 2013, 2013
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 264.17
Quantity: 2 available
Add to basketItem description from seller
Neuware - Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM.Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions.Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors' detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies.\* Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm\* Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms\* Benefits from extensive use of the authors' detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.…
Seller Inventory # 9781119994336
- Title
- Structural Health Monitoring
- Author
- Farrar
- Publisher
- John Wiley & Sons Jan 2013
- Publication year
- 2013
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 1119994330
- ISBN 13
- 9781119994336
- Item weight
- 1,292 grams
- Dimensions
- 250x175x39 mm
Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions.
Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors’ detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies.
- Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm
- Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms
- Benefits from extensive use of the authors’ detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.
"Synopsis" may belong to another edition of this title.
About the Author
Keith Worden, University of Sheffield, UK is Head of the Dynamics Research Group in the Department of Mechanical Engineering at the University of Sheffield. His research interests lie in the applications of advanced signal processing and machine learning methods to structural dynamics. He has authored over 400 research publications including two co-authored books on nonlinear structural dynamics and nonlinear system identification, two book chapters and over 130 refereed journal papers. He serves on the editorial boards of 2 international journals: Journal of Sound and Vibration and Mechanical Systems and Signal Processing. He was awarded "2004 Person of the Year" (jointly with W.J. Staszewski) awarded by Structural Health Monitoring journal for outstanding contribution in the field.
"About the title" may belong to another edition of this title.
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