Evaluation of bearing performance degradation based on MMFE and extensible k-medoids clustering algorithm

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (17) : 123-130.

ZHAO Congcong1, LIU Yumei2, ZHAO Yinghui3, BAI Yang4, SHI Jihong1

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Abstract

Traditional bearing performance degradation assessment methods usually makes qualitative analysis, and takes vertical signal of bearing as the research object, ignoring the correlationship between vibration signals in different directions. To resolve this problem, the multivariable multi-scale fuzzy entropy (MMFE) for evaluating the complexity of multi-channel time series was introduced into the feature extration of bearing operating state. Extenics and k-medoids clustering algorithm were combined to establish the bearing performance degradation evaluation model. By clustering the samples in bearing normal state with k-medoids algorithm, the clustering center of bearing normal state was obtained. According to the Euclidean distance between the tested samples and the normal state clustering center, the extension sets were determined. Furthermore, based on the extension correlation function, the quantitative assessment model of bearing performance degradation was established. The bearing whole life fatigue test was used to verify the effectiveness of the proposed method. The results show that the proposed method can detect the early performance degradation of bearing effectively, evaluate the bearing performance degradation quantitatively as well.
Key words: bearing; performance degradation; multivariable multi-scale fuzzy entropy; k-medoids algorithm; extenics

Key words

bearing / performance degradation / multivariable multi-scale fuzzy entropy / k-medoids algorithm / extenics

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ZHAO Congcong1, LIU Yumei2, ZHAO Yinghui3, BAI Yang4, SHI Jihong1. Evaluation of bearing performance degradation based on MMFE and extensible k-medoids clustering algorithm[J]. Journal of Vibration and Shock, 2022, 41(17): 123-130

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