Bearing fault diagnosis method based on deep metric learning

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (15) : 25-31.

LI Xiaojuan1,2, XU Zengbing1,2, XIONG Wen3, WANG Zhigang1,2, TAN Junjie1,2

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Abstract

As the intra-class scatter and inter-class similarity are big in bearing fault data, which constrains the diagnostic accuracy, a new method of deep metric learning for fault diagnosis is proposed. The deep neural network (DNN) is used to adaptively extract the fault features, and the marginal Fisher analysis method based on Euclidean distance is used to optimize the features, then, the BPNN classifier is added to the top-level feature output layer of the constructed deep metric network (DMN) to fine tune the parameters and realize fault classification and recognition. Finally, diagnostic analysis on bearing fault experimental data of different fault types and different fault severity verified that the method can effectively diagnose bearing faults with high precision and the effect is better than traditional deep belief network (DBN) fault diagnosis method as well as the common time-domain statistical features combined with support vector machine (SVM) classification fault diagnosis method.

Key words

deep metric learning / bearing / fault diagnosis / similarity

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LI Xiaojuan1,2, XU Zengbing1,2, XIONG Wen3, WANG Zhigang1,2, TAN Junjie1,2. Bearing fault diagnosis method based on deep metric learning[J]. Journal of Vibration and Shock, 2020, 39(15): 25-31

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