基于VMD-ICMSE和半监督判别SOINN L-Isomap的滚动轴承故障诊断

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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)

Received: 05 July 2018 Published: 15 February 2020

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	Articles by authors
	QI Xiaoli
	WANG Zhenya
	WU Baolin
	YE Xudan
	PAN Ziwei

Cite this article:

QI Xiaoli,WANG Zhenya,WU Baolin, et al. 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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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2020/V39/I4/252

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