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Joint denoising method of rolling bearing based on IVMD and XWT
Published:2023-04-20 author:​WANG Peng-bo, LIU Zi-ran, LIU Yu-ming,et al.​ Browse: 1349 Check PDF documents
Joint denoising method of rolling bearing based on IVMD and XWT


WANG Peng-bo, LIU Zi-ran, LIU Yu-ming, LV Zhen-li

(School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China)


Abstract: Aiming at the problem that the early fault signal of bearing was easily submerged in noise,and the fault feature was difficult to extract,a joint denoising signal processing method based on cross wavelet transform (XWT) and improved variational mode decomposition (IVMD) was proposed.Firstly, XWT was performed on the original signal of two channels to obtain the wavelet coherence spectrum. The optimal mode number K was determined by the envelope spectrum curve, and the traditional VMD was optimized to IVMD. The signal with large kurtosis value in the two channels was decomposed into multiple intrinsic mode components (IMFs) by IVMD,
the XWT was performed for each IMF and signals with large kurtosis value.Then, the obtained wavelet coherence spectrum was compared with the wavelet coherence spectrum of the dual channel original signal, and the identified noise components were removed from the original signal to achieve the purpose of noise reduction and fault feature enhancement.Finally, K-nearest neighbor (KNN) algorithm was used to classify rolling bearing faults, and the fault recognition rate reached 97.51%, which was 10.83% and 4.62% higher than IVMD and VMD-XWT respectively. The results show that this method can reduce noise interference and extract early fault information of rolling bearing better.

Key words:  rolling bearing fault diagnosis; fault feature extract; noise reduction; fault feature enhancement; cross wavelet transform(XWT); improved variational modal decomposition(IVMD); Knearest neighbor(KNN) algorithm;intrinsic mode components(IMFs)
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