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Rolling Bearing Degradation State Identification Based on LCD Relative Spectral Entropy

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Abstract

In the interest of obtaining an effective bearing degradation feature from complex, nonlinear, and nonstationary vibration signals, a new analytical methodology based on local characteristic-scale decomposition (LCD) and relative entropy theory is proposed. On the one hand, LCD is a new and relatively excellent time-frequency analysis method to analyze practical vibration signals polluted by noise. On the other hand, relative entropy theory is a good way to characterize different degradation states by calculating the probability distribution difference between the degradation signals and the normal signal. Combining the above two theories, two new degradation features named LRNE and LRQE are extracted to indicate the bearing degradation trend from normal state to even failure state. The noise resistance ability and extensive applicability of both the features are verified by simulation signal. For further analysis of experimental vibration signals, the two features have a satisfying performance to characterize different bearing degradation states. With the help of gray relational analysis and fuzzy C-means clustering, the proposed two characteristics can identify different bearing degradation states of inner ring fault mode with high accuracy. In the end, the two features are applied to doing bearing failure analysis with the full-life bearing data. The results show that the LRNE and LRQE are sensitive to bearing degradation trend in the whole life of bearing.

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Acknowledgments

This project is supported by National Natural Science Foundation of China (Grant No. 51541506).

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Authors and Affiliations

  1. Mechanical Engineering College, No.97, Heping West Road, Shijiazhuang, 050003, Hebei, China

    He Yu, Hongru Li & Baohua Xu

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  1. He Yu
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  2. Hongru Li
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  3. Baohua Xu
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Correspondence to He Yu.

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Yu, H., Li, H. & Xu, B. Rolling Bearing Degradation State Identification Based on LCD Relative Spectral Entropy. J Fail. Anal. and Preven. 16, 655–666 (2016). https://doi.org/10.1007/s11668-016-0133-y

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  • DOI: https://doi.org/10.1007/s11668-016-0133-y

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