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Failure Characteristics Analysis and Fault Diagnosis for Liquid Rocket Engines de Wei Zhang

Failure Characteristics Analysis and Fault Diagnosis for Liquid Rocket Engines

Autor Wei Zhang
en Limba Engleză Paperback – 30 mai 2018
This book concentrates on the subject of health monitoring technology of Liquid Rocket Engine (LRE), including its failure analysis, fault diagnosis and fault prediction. Since no similar issue has been published, the failure pattern and mechanism analysis of the LRE from the system stage are of particular interest to the readers. Furthermore, application cases used to validate the efficacy of the fault diagnosis and prediction methods of the LRE are different from the others. The readers can learn the system stage modeling, analyzing and testing methods of the LRE system as well as corresponding fault diagnosis and prediction methods. This book will benefit researchers and students who are pursuing  aerospace technology, fault detection, diagnostics and corresponding applications.
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Paperback (1) 61389 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 30 mai 2018 61389 lei  6-8 săpt.
Hardback (1) 61993 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 6 iun 2016 61993 lei  6-8 săpt.

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Specificații

ISBN-13: 9783662569948
ISBN-10: 3662569949
Ilustrații: XIV, 401 p. 153 illus., 61 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.58 kg
Ediția:Softcover reprint of the original 1st ed. 2016
Editura: Springer Berlin, Heidelberg
Colecția Springer
Locul publicării:Berlin, Heidelberg, Germany

Cuprins

Introduction.- Failure pattern and corresponding mechanism analysis of liquid Rocket engines (LRE).- Analysis method of failure model for LRE.- Failure characteristics analysis of LRE.- Fault diagnosis of LRE based on artificial neural net.- Fault diagnosis method based on Wavelet transform.- Fault diagnosis method based on artificial immune system.- Fault diagnosis method based on fuzzy theory.- Fault analysis and diagnosis method based on statistical learning theory.- Fault diagnosis method based on hide Markov model.- Fault prediction method of LRE.

Recenzii

“This book is best classified as an exercise in applied mathematics; it presents various mathematical methods that can be applied to the problem of fault diagnostics in liquid-fueled rocket engines. The techniques include those based on neural networks, wavelet transforms, fuzzy sets, statistical learning theory, hidden Markov models, and time series analysis. … Summing Up: Recommended. Professionals and practitioners only.” (A. M. Strauss, Choice, Vol. 54 (4), December, 2016)

Notă biografică

Wei Zhang received the specialized M.S. degrees in mechanics from Xi’an Jiaotong University in 1995, and the Ph.D. degrees in ordnance science and technology from Xi’an High-tech institute, Shaanxi, China, in 2003. He is currently a Professor of aeronautical science and technology in Xi’an High-tech institute, Shaanxi, China. His research interests include non-destructive testing technology, condition monitoring, fault diagnosis and signal processing. He is one of the fifth executive directors of the fault diagnosis professional committee in the Chinese Society of Vibration Engineering. The funding of the projects he has involved or been responsible for has exceeded $ 1000,000. He has published 3 books and about 150 papers. Many papers have been indexed by SCI and EI.

Gan Tian received the B.S. degrees in mechanics from Xi’an High-tech institute, Shaanxi, China, in 2003, and the specialized M.S. degrees in aeronautical and astronautical science and technology from Xi’an High-tech institute, Shaanxi, China, in 2009. He is currently a Lecturer of aeronautical science and technology in Xi’an High-tech institute, Shaanxi, China. His research interests include non-destructive testing technology and  fault diagnosis.

Zhigao Xu received the B.S. degrees in mechanics from Xi’an High-tech institute, Shaanxi, China, in 1999, the specialized M.S. degrees in aeronautical and astronautical science and technology from Xi’an High-tech institute, Shaanxi, China, in 2003 and the Ph.D. degrees in ordnance science and technology from Xi’an High-tech institute, Shaanxi, China, in 2009. He is currently an Associate Professor of aeronautical science and technology in Xi’an High-tech institute, Shaanxi, China. His research interests include fault diagnosis and signal processing.

Zhengwei Yang received the B.S. degrees in mechanics from Xi’an High-tech institute, Shaanxi, China, in 2005, and the Ph.D. degrees in ordnance science and technology from Xi’an High-tech institute, Shaanxi, China, in 2011. He is currently a Lecturer of aeronautical science and technology in Xi’an High-tech institute, Shaanxi, China. His research interests include non-destructive testing technology, condition monitoring, and signal processing.

Textul de pe ultima copertă

This book concentrates on the subject of health monitoring technology of Liquid Rocket Engine (LRE), including its failure analysis, fault diagnosis and fault prediction. Since no similar issue has been published, the failure pattern and mechanism analysis of the LRE from the system stage are of particular interest to the readers. Furthermore, application cases used to validate the efficacy of the fault diagnosis and prediction methods of the LRE are different from the others. The readers can learn the system stage modeling, analyzing and testing methods of the LRE system as well as corresponding fault diagnosis and prediction methods. This book will benefit researchers and students who are pursuing  aerospace technology, fault detection, diagnostics and corresponding applications.

Caracteristici

The first book available on the fault characteristic analysis of LRE Includes detailed modeling program of the LRE whole system with different faults Involves several applications of artificial neural net (ANN), wavelet analysis, artificial immune system (AIS), fuzzy theory, statistical learning theory, hide-markov model (HMM) in the fault diagnosis of the LRE Involves several applications of the time series analysis, gray model, support vector machine (SVM) in the fault prediction of the LRE Includes supplementary material: sn.pub/extras