NDLI: An approach for bearing fault diagnosis based on PCA and multiple classifier fusion

Content Provider	IEEE Xplore Digital Library
Author	Min Xia Fanrang Kong Fei Hu
Copyright Year	2011
Description	Author affiliation: Department of Precision Machinery and Instrumentation, University of Science and Technology of China, Hefei, China (Min Xia; Fanrang Kong; Fei Hu)
Abstract	The purpose of this paper is to propose a new system, with both high efficiency and accuracy for fault diagnosis of rolling bearing. After pretreatment and choosing sensitive features of different working conditions of bearing from both time and frequency domain, principal component analysis(PCA) is conducted to compress the data dimension and eliminate the correlation among different statistical features. The first several principal components are sent to the classifier for recognition. However, recognition method with a single classifier usually has only a limited classification capability that is insufficient for real applications. An ongoing strategy is the decision fusion techniques. The system proposed in this paper develops a decision fusion algorithm for fault diagnosis, which integrates decisions of multiple classifiers. First, the front four principle components are chosen as input of individual classifier. A selection process of the classifiers is then operated on the basis of correlation measure for the purpose of finding an optimal sequence of them. Finally, classifier fusion algorithm based on Bayesian belief method is applied to generate the final decision. The result of experiments show that this new bearing fault diagnosis system recognize different working conditions of bearing more accurately and more stably than a single classifier does, which demonstrates the high efficiency of the proposed system.
Starting Page	321
Ending Page	325
File Size	282570
Page Count	5
File Format	PDF
ISBN	9781424486229
e-ISBN	9781424486250
DOI	10.1109/ITAIC.2011.6030215
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Vibrations Accuracy Correlation Bayesian methods Multiple classifier fusion Frequency domain analysis Rolling bearing Principal component analysis PCA
Content Type	Text
Resource Type	Article

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