NDLI: Bearing fault diagnosis based on negative selection algorithm of feature extraction and neural network

Content Provider	IEEE Xplore Digital Library
Author	Xiaoping Ma Xiaobin Wei Fengshuan An Peizhao Su
Copyright Year	2010
Description	Author affiliation: School of Information and Electrical Engineer, China University of Mining and Technology, Xuzhou Jiangsu, 221008, China (Xiaoping Ma; Xiaobin Wei; Fengshuan An; Peizhao Su)
Abstract	As the fault diagnosis based on neural network needs typical features samples, the paper proposes a hybrid fault diagnosis method which integrates the RNSA (real-valued negative selection algorithm) and the radial basis function network. In this method, we choose typical fault samples (generated by Negative selection algorithm) as the inputs of the neural network, which solves the difficulty of obtaining typical samples, then extracting feature extraction from rolling bearing vibration signal with wavelet packet analysis is finished. At last, the fault samples (generated by RSNA) have been used to validate the new algorithm, and the accuracy is up to 97.2%, which verifies validity of the algorithm.
Starting Page	3938
Ending Page	3941
File Size	168606
Page Count	4
File Format	PDF
ISBN	9781424451814
DOI	10.1109/CCDC.2010.5498450
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Wavelet analysis Wavelet packet analysis Fault diagnosis Neural networks Rolling bearings Radial basis function networks Feature extraction Signal generators Wavelet packets Radial basis function network Real value negative selection algorithm Signal analysis
Content Type	Text
Resource Type	Article

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