基于Renyi熵和K-medoids聚类的轴承性能退化评估

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Abstract Bearing performance degradation assessment (PDA) underlies residual useful life prediction and maintenance decision-making. In bearing fault diagnosis and PDA, a commonly used method is clustering analysis, among which the K-medoids clustering is not susceptible to extreme data. The amalgamation of the K-medoids clustering and a membership function are expected to give a health indicator with a determined value range for PDA. Vibration signals are first decomposed into multiple sub-bands on which a denoising algorithm is then applied, resulting in a series of denoised sub-band signals. The Renyi entropy is a nonlinear parameter able to identify subtle change in signals. As such, Renyi entropies of denoised sub-band signals constitute the feature vectors presented to the combined model of K-medoids based clustering and a membership function. Resultant health indicator was used to depict bearing health condition with the help of an adaptive threshold. Results on artificially induced faults and bearing run-to-failure data demonstrate that the proposed method is able to track the progress of bearing faults and detect them at incipient stage.

Key words： rolling bearing Renyi entropy K-medoids performance degradation

Received: 05 June 2019 Published: 15 October 2020

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2020/V39/I20/24

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