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Fault diagnosis of cross-roller bearings based on MRSVD-SVD and VPMCD

Published:2023-03-23 author:HE Dong-kang, GAN Lin, LEI Zhi-jie, et al. Browse: 444 Check PDF documents

Fault diagnosis of cross-roller bearings based on MRSVD-SVD and VPMCD

HE Dong-kang1, GAN Lin1, LEI Zhi-jie1, DENG Qi-gui1, HE Jie2,3

(1.School of Mechanical and Electrical Engineering, Liuzhou Vocational & Technical College,

Liuzhou 545006,China; 2.Special Equipment Safety Inspection Institute in Yunnan Province,

Kunming 650228, China; 3.Yunnan Huimin Labor Service Co.,Ltd., Kunming 650228, China)

Abstract: Aiming at the problem of insufficient singular value resolution of singular value decomposition(SVD)extracting the weak fault information from the cross-roller bearings for industrial robots, a new method based on maximum resolution singular value decomposition (MRSVD)-SVD and variable predictive model class discriminate (VPMCD) fault diagnosis method for industrial robot cross-roller bearings was proposed. Firstly, one-dimensional vibration signals were constructed into Hankel matrix based on the principle of maximum singular value resolution, the Hankel matrix was decomposed by SVD to obtain singular value sequence. According to the singular value curvature spectrum theory, the effective singular value was selected to reconstruct the denoised signal with high signalnoise ratio (SNR). The phase space matrix was constructed from reconstructed signals. The fault feature components were obtained by quadratic SVD processing of the reconstructed signals. Then, the characteristic parameters of fault feature components were calculated to construct feature vectors. Finally, VPMCD was used to analyze feature vectors and identify fault types. The experimental results show that the method is applied to fault diagnosis of industrial robot cross-roller bearings. The fault type identification accuracy is 98.66%, which is 9% higher than the method combining SVD and resonance demodulation. It is shown that the proposed method improves the singular value resolution by constructing the maximum singular value resolution matrix, and completely extracts the weak fault feature components of the vibration signals of the industrial robot cross-roller bearing, thus achieving higher fault type identification accuracy.

Key words: rolling bearing; cylindrical roller bearings; maximum resolution singular value decomposition(MRSVD); singular value decomposition(SVD); variable predictive model class discriminate(VPMCD); Hankel matrix

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