Statistical Index Development from Time Domain for Rolling Element Bearings
Volume 10, Number 3, May 2014 - Paper 08 - pp. 313-324
YUAN FUQING and UDAY KUMARDivision of Operation and Maintenance, Luleå University of Technology, SE-971 87 Lulea, SWEDEN
(Received on November 30, 2013, revised on March 19, and March 30, 2014)
Feature extraction is crucial to efficiently diagnose fault. This paper discusses a number of time-domain statistical features, including Kurtosis or the Crest Factor, the Mean by Deviation Ratio (MDR), and Symbolized Sequence Shannon Entropy (SSSE). The SSSE reflects the spatial distribution of the signal which is complementary with the statistical features. A new feature, Normalized Normal Negative Likelihood (NNNL), is used to improve the Normal Negative Likelihood (NNL). A Separation Index (SI) called the Extended SI (ESI) evaluates the performance of each feature and to remove noise feature. The Multi-Class Support Vector Machine (MSVM) recognizes bearing defect patterns. A numerical case is presented to demonstrate these features, their feature subset selection method and the pattern recognition method. The MSVM is used to detect three different types of bearing defects: defects in the inner race, outer race and bearing ball.
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