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.
Highlights
Identification and elimination of fault conditions in rotating machinery significantly increase the working efficiency and service life
The results show that the effectiveness of the method proposed is achieved by wavelet threshold denoising (WTD), but by ensemble empirical mode decomposition (EEMD) decomposition, selection of intrinsic modal function (IMF) components, WTD and independent component analysis (ICA)
To verify the effectiveness of the method proposed in this paper, using local mean value decomposition (LMD)-WTD [25] and EEMD-ICA [40] to process the frequency spectrum of vibration signals are shown in Figs 21 and 22
Summary
Identification and elimination of fault conditions in rotating machinery significantly increase the working efficiency and service life. Based on the above problems, and considering the weak early fault signals of the equipment, variable working conditions, and the presence of external environmental interference, a multistage noise reduction method based on EEMD to realize the feature extraction of weak signals is proposed, which overcomes the defect that a single method is difficult to achieve high-precision fault detection. The early fault signal strength of rotating machinery is weak, contains a lot of noise, and is nonlinear and non-stationary, which makes it difficult to extract fault features. The vibration signal of the faulty equipment is decomposed by EEMD to obtain a series of IMF components, it shows the local signal of the fault This signal contains a lot of interference noise (e.g., Gaussian white noise, impact noise, interference noise between devices), which is not conducive to highlighting the fault frequency.
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