Feature Extraction of Weak Fault for Rolling Bearing Based on Improved Singular Value Decomposition

doi:10.3901/JME.2022.17.156

Abstract

Abstract: A novel fault diagnosis method based on improved singular value decomposition (ISVD) is proposed to extract the early weak fault feature of rolling element bearings submerged in strong background noise and harmonic interference. Firstly, according to the characteristics of sinusoidal signal, composite sinusoidal signal, periodic impact signal and the formation principle of singular value pairs (SVP), the optimization selection principles of improved embedding dimension of Hankel matrix are proposed respectively, and the quantization range of this parameter is defined. Then the optimal embedding dimension of singular value decomposition (SVD) is determined. The method can adaptively match the optimal embedding dimension of Hankel matrix of SVD. Then the signal decomposition strategy of SVP distribution is obtained. Secondly, combining with the energy of harmonic interference and SVP distribution, the sub-signals contained the weak fault information are located. Finally, the fault sub-signals are reconstructed by the inverse diagonal average method, and the diagnosis results are obtained by the envelope spectrum analysis. The feasibility and effectiveness of the proposed method are verified through the analysis results of simulated rolling bearing fault and multiple experiment signals.

Key words: singular value decomposition, envelope analysis, feature extraction, rolling bearing

CLC Number:

TH17

CUI Lingli, LIU Yinhang, WANG Xin. Feature Extraction of Weak Fault for Rolling Bearing Based on Improved Singular Value Decomposition[J]. Journal of Mechanical Engineering, 2022, 58(17): 156-169.

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