A performance degradation condition recognition method based on improved pattern spectrum entropy and fuzzy c-means

Abstract

In allusion to performance degradation condition recognition issue for rolling bearing, a method based on improved pattern spectrum entropy (abbreviated as IPSE) and fuzzy C-means algorithm (abbreviated as FCM) is proposed in this paper. In the course of performance degradation feature extraction, morphological corrosion operator is introduced and IPSE is proposed as the degradation feature parameter in describing bearing performance degradation degree. Simulation analysis is processed and shows that, the value of IPSE will increase correspondingly along with the deepening of the degradation degree and the relevance between IPSE and degradation degree is stable. On this basis, in consideration of the fuzzy character of performance degradation condition boundary, FCM is introduced in degradation condition recognition and the degradation condition could be recognized effectively in line with maximum subordination degree principle. Rolling bearing fatigue life enhancement testing was carried out in Hangzhou Bearing Test & Research Center, the whole life data was gathered and applied in this paper, the result shows that the proposed technique has an excellent effect.