基于变分模态分解与流形学习的滚动轴承故障特征提取方法

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Abstract A method for early fault diagnosis of rolling bearings based on the variational mode decomposition (VMD) combined with the local tangent space alignment (LTSA) algorithm was proposed.Firstly,the VMD algorithm was used to decompose vibration signals of a rolling bearing under different operational conditions,and several components most correlated to the original signal were screened out by solving the instantaneous frequency mean and plotting the feature curve.Then,the time domain index of the effective modal component and the frequency domain index formed with the wavelet packet frequency band decomposition energy were extracted.After these two indexes were combined to primarily extract higher dimensional fault features,LTSA was used to extract fault features again.Finally,the extracted fault features were inputted into a K-means classifier to recognize fault types.Through the contrastive test analysis of cylindrical roller bearings’fault diagnosis,it was shown that compared with the method of time frequency features extraction+LTSA and the method of EMD+LTSA,the method of VMD+LTSA has more advantages in classification effect and recognition accuracy; the LTSA algorithm has the best sensitivity to fault features after dimension reduction compared to the algorithms of PCA,LPP,LE,ISOMAP and LLE; the proposed method has a certain superiority in the fault diagnosis of cylindrical roller bearings.

Key words： variational mode decomposition manifold learning local tangent space alignment algorithm fault diagnosis cylindrical rolling bearing

Received: 29 August 2017 Published: 28 November 2018

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	Articles by authors
	QI Xiaoli
	YE Xudan
	CAI Jianglin
	ZHENG Jinde
	PAN Ziwei
	ZHANG Xingquan

Cite this article:

QI Xiaoli,YE Xudan,CAI Jianglin, et al. Fault feature extraction method of rolling bearings based on VMD and manifold learning[J]. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(23): 133-140.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2018/V37/I23/133

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