Bearing fault diagnosis based on semi-supervised kernel local Fisher discriminant analysis using pseudo labels

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (17) : 1-9.

TAO Xinmin, REN Chao, XU Lang, HE Qing, LIU Rui, ZOU Junrong

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Abstract

In order to solve the problems of high-dimensionality, strong-relevance, and information redundancy due to the results of information fusion in bearing fault diagnosis domains, a novel bearing fault diagnosis approach based on semi-supervised kernel local fisher discriminant analysis (SS-KLFDA) using pseudo labels is presented in this paper. In the proposed approach, to sufficiently utilize a large amount of unlabeled samples to improve the discriminantperformance, we firstly adopt density peak clustering to acquire the pseudo cluster labels for unlabeled samples and then add regularization terms into both the local within-class and local between-class scatter matrices to preserve the local cluster structure for the unlabeled data. Finally, the added regularization terms are incorporated into the local within-class and local between-class scatter matrices concerning labeled samples to formulate the objective function and the final projection matrix are obtained by maximizing the objective function. In addition, in order to accommodate for nonlinearly separate problems, we further give a kernelized version of SS-KLFDA using pseudo labels in this study. In the experiment, the proposed approach is compared with other existing dimensionality reduction methods under different reduced dimensions, different feature sets, and different combined classifiers scenarios. The results show that the proposed approach could greatly increase the classification accuracies of all combined classifiers and possesses the best discrimination capacity among the dimension-reduced features, which can effectively improve the fault diagnosis performance.

Key words

Fault Diagnosis / Fisher Discriminant Analysis / Dimension Reduction / Semi-Supervised

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TAO Xinmin, REN Chao, XU Lang, HE Qing, LIU Rui, ZOU Junrong. Bearing fault diagnosis based on semi-supervised kernel local Fisher discriminant analysis using pseudo labels[J]. Journal of Vibration and Shock, 2020, 39(17): 1-9

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