Dynamic mode decomposition and its application in early bearing fault diagnosis

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (12) : 313-320.

WEN Ming1,2, DANG Zhang1,2,3, YU Zhen1,2, L Yong1,2， WEI Guoqian1,2

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Abstract

Rolling bearing is a key component in the equipment manufacturing industry, its working status in the service process directly determines the performance, quality and reliability of the host products. Effective supervision and detection technology for early fault of bearing is an important guarantee for the equipment preventive maintenance. An improved dynamic mode decomposition (DMD) algorithm was proposed and applied to early fault extraction of bearing signals. The original time series collected by the sensor was decomposed by DMD after projection denoising, then the soft threshold method was adopted to obtain several single-frequency modes adaptively according to the noise level of original signal. Multi-scale permutation entropy values of these modes were calculated next, the low-rank components in the original dynamic system were picked out by the threshold method. After the reconstruction, the early fault frequencies were extracted. The effectiveness of the proposed algorithm was verified with experimental study on simulation signal and measured signal.

Key words

dynamic mode decomposition / multi-scale permutation entropy / adaptive truncated rank / feature extraction

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WEN Ming1,2, DANG Zhang1,2,3, YU Zhen1,2, L Yong1,2， WEI Guoqian1,2. Dynamic mode decomposition and its application in early bearing fault diagnosis[J]. Journal of Vibration and Shock, 2022, 41(12): 313-320

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