INTELLIGENT FAULT DIAGNOSIS OF ROLLING ELEMENT BEARING BASED ON SVMS AND STATISTICAL CHARACTERISTICS

摘要

In this paper, the statistical characteristics of time, fre-quency and time-frequency domain are applied to discriminate various fault types and evaluate various fault conditions of rolling element bearing, and the classification performance of them is evaluated by using SVMs. Experimental results showed that the statistical characteristics Mean, Variance, Root, RMS and Peak of the 2~5 sub frequency bands in frequency domain ob-tain higher classification accuracy rate on all the fault datasets than the statistical characteristics in the whole time and fre-quency domain. Wavelet packet decomposition is an efficient time-frequency analysis tool, and it can decompose the original signal into independent frequency bands. Experiment on the sta-tistical characteristics of the 5th level wavelet packet decompo-sition showed that the statistical characteristics Variance, Root, RMS and Peak can discriminate various fault types and evalu-ate various fault conditions well on all the datasets. Compared with the statistical characteristics of sub frequency bands in fre-quency domain, the classification performance of the statistical characteristics of the wavelet packet transform is a little lower than that of the statistical characteristics of sub frequency bands in frequency domain.