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. Basic pattern spectrum analysis is improved by introducing morphological corrosion operator and IPSE is proposed as the degradation feature parameter in describing bearing performance degradation degree. Simulation analysis shows that IPSE value will increase monotonously along with the deepening of the degradation degree. IPSE and degradation degree has a stable relevance. On this basis, in consideration of the fuzzy character of performance degradation condition boundary, FCM is introduced in degradation condition recognition so that 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 using the proposed technique. The classification coefficient reaches 0.9849 and average fuzzy entropy gets 0.0239 for training set clustering, meanwhile, the whole recognition ratio reaches 90% for testing set. The analysis shows that the technique has a good clustering effect and an acceptable recognition result.
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