A rolling bearing fault diagnosis method based on VMD-ICMSE and semi-supervised discriminant SOINN L-Isomap

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (4) : 252-260.

QI Xiaoli, WANG Zhenya, WU Baolin, YE Xudan, PAN Ziwei

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Abstract

For the difficulty that extracting fault features from the nonlinear and non-stationary vibration signals of rolling bearings, a fault diagnosis method of rolling bearing based on semi-supervised discriminant self-organizing incremental neural network landmark Isomap (SSDSL-Isomap) was proposed.Firstly, the fault features of vibration signals were extracted from the complex domain by using the improved composite multiscale sample entropy based on variational mode decomposition (VMD-ICMSE), and the high-dimensional fault feature set was constructed.Secondly, the SSDSL-Isomap method was used to reduce the dimension of the high-dimensional fault feature set, and the low-dimensional and sensitive feature subset was extracted.Finally, low-dimensional features were input into a particle swarm optimization extreme learning machine (PSO-ELM) classifier to recognize fault types.The VMD-ICMSE method integrates the advantages of VMD in adaptive nonlinear signals decomposition and ICMSE to measure the complexity of time series, and improves the ability of fault feature extraction.The SSDSL-Isomap method considers global manifold structure information, semi-supervised double-constraint graph construction and SOINN landmark selection; therefore, it enhances the ability of fault classification.Experimental results of fault diagnosis on self-aligning ball bearings show that the identifying rate of the proposed method is 100%.

Key words

feature diagnosis / rolling bearing / SSDSL-Isomap / variational mode decomposition（VMD） / improved composite multiscale sample entropy(ICMSE) / particle swarm optimization extreme learning machine(PSO-ELM)

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QI Xiaoli, WANG Zhenya, WU Baolin, YE Xudan, PAN Ziwei. A rolling bearing fault diagnosis method based on VMD-ICMSE and semi-supervised discriminant SOINN L-Isomap[J]. Journal of Vibration and Shock, 2020, 39(4): 252-260

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