Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

Kangping Gao, Xinxin Xu, Jiabo Li, Shengjie Jiao and Ning Shi

PLOS ONE, 2021, vol. 16, issue 7, 1-24

Abstract: Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD, and the main intrinsic modal components (IMF) are selected using comprehensive evaluation indicators; the second layer of filtering uses wavelet threshold denoising (WTD) to process the main IMF components. Finally, the virtual noise channel is introduced, and FastICA is used to de-noise and unmix the IMF components processed by the WTD. Next, perform spectral analysis on the separated useful signals to highlight the fault frequency. The feasibility of the proposed method is verified by simulation, and it is applied to the extraction of weak signals of faulty bearings and worn polycrystalline diamond compact bits. The analysis of vibration signals shows that this method can efficiently extract weak fault characteristic information of rotating machinery.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0254747

DOI: 10.1371/journal.pone.0254747

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