Rolling Bearing Performance Degradative State Recognition Based on Mathematical Morphological Fractal Dimension andFuzzy Center Means

doi:10.3969/j.issn.1000-1093.2015.10.022

Abstract

Abstract: In allusion to the degenerative state recognition of rolling bearing, a performance degenerative recognition method based on mathematical morphological fractal dimension (MMFD) and fuzzy center means (FCM) is proposed by combining mathematical morphology and fuzzy assemble theory. MMFD is calculated for the performance degenerative feature of rolling bearing to describe its complexity and irregularity in the view of fractal. In consideration of the fuzziness among different performance degradation boundaries, FCM is introduced into fuzzy clustering for characteristic index, and the performance degradation could be recognized effectively in line with maximum subordinate principle. The fatigue life enhancement test of rolling bearing was carried out to gather the whole life data at Hangzhou Bearing Test & Research Center. The method is applied to the whole life data of rolling bearing, the overall state successful recognition rate reachs 96%. The results show that the method has a small calculating cost and a high efficiency, and can efficiently identify the performance degenerative state of rolling bearings.

Key words: mechanics, feature extraction, mathematics morphology, fuzzy clustering, degenerative state recognition, rolling bearing

CLC Number:

TP206
TP911

WANG Bing, LI Hong-ru, CHEN Qiang-hua, XU Bao-hua. Rolling Bearing Performance Degradative State Recognition Based on Mathematical Morphological Fractal Dimension andFuzzy Center Means[J]. Acta Armamentarii, 2015, 36(10): 1982-1990.

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