Paper Info
Title
An intelligent fault diagnosis method based on wavelet packer analysis and hybrid support vector machines
Abstract
In this paper, a new intelligent method for the fault diagnosis of the rotating machinery is proposed based on wavelet packet analysis (WPA) and hybrid support machine (hybrid SVM). In fault diagnosis for mechanical systems, information about stability and mutability can be further acquired through WPA from original signal. The faulty vibration signals obtained from a rotating machinery are decomposed by WPA via Dmeyer wavelet. A new multi-class fault diagnosis algorithm based on 1-v-r SVM approach is proposed and applied to rotating machinery. The extracted features are applied to hybrid SVM for estimating fault type. Compared to conventional back-propagation network (BPN), the superiority of the hybrid SVM method is shown in the success of fault diagnosis. The test results of hybrid SVM demonstrate that the applying of energy criterion to vibration signals after WPA is a very powerful and reliable method and hence estimating fault type on rotating machinery accurately and quickly.
Year
DOI
Venue
2009
10.1016/j.eswa.2009.03.063
Expert Syst. Appl.
Keywords
Field
DocType
hybrid svm,1-v-r approach,multi-fault diagnosis,new intelligent method,fault type,hybrid svm method,wavelet packet analysis,wavelet packer analysis,fault diagnosis,hybrid support machine,intelligent fault diagnosis method,reliable method,dmeyer wavelet,hybrid support vector machines,new multi-class fault diagnosis,1-v-r svm approach,hybrid support vector machine,support vector machine,mechanical systems
Data mining,Computer science,Support vector machine,Artificial intelligence,Vibration,Wavelet packet analysis,Mechanical system,Machine learning,Wavelet
Journal
Volume
Issue
ISSN
36
10
Expert Systems With Applications
Citations 
PageRank 
References 
11
1.11
11
Authors
2
Name
Order
Citations
PageRank
Guang-ming Xian1514.58
Bi-qing Zeng2184.83