Intelligent Fault Diagnostic System of Induction Motor | Based on Feature Extraction & Feature Selection

Tian Han

ISBN 10: 3659514624 / ISBN 13: 9783659514623

Published by LAP LAMBERT Academic Publishing, 2014

About this Item

Intelligent Fault Diagnostic System of Induction Motor | Based on Feature Extraction & Feature Selection | Tian Han | Taschenbuch | 256 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659514623 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 105197587

Bibliographic Details

• Title: Intelligent Fault Diagnostic System of Induction Motor | Based on Feature Extraction & Feature Selection
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2014
• Language: English
• ISBN 10: 3659514624
• ISBN 13: 9783659514623
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 16D millimeters 220 millimeters width by 150 millimeters height by 16 millimeters depth
• Weight: 399 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Early fault diagnosis can increase machinery availability and performance, reduce consequential damage, prolong machine life, and reduce spare parts inventories and breakdown maintenance. In this book, one intelligent fault diagnostic system is proposed based on feature extraction and selection techniques. Features are calculated from many domains: time domain, frequency domain, cepstrum domain and wavelet domain. In this way, the information of raw data is kept at best to meet different analysis methods in future. Principal component analysis and linear discriminant analysis, two feature extraction methods are introduced. Feature selection methods, individual feature evaluation and genetic algorithm, are compared. They are used to reduce feature dimensionality and improve system performance. The proposed system is applied to fault diagnosis of induction motors as a real application. The results show that the proposed system, combining feature extraction with feature selection, has fast training procedure, high classification rate and compact structure. It is suitable for motor condition monitoring and fault diagnosis.

About the Author

Doctor Tian Han is working in the School of Mechanical Engineering at University of Science and Technology Beijing in China. He received his PhD degree in 2005. Dr. Han's main research fields cover machine dynamics, vibration engineering and condition monitoring and diagnostics in rotating machinery. He has published over 30 research papers.