Multi-working condition rolling bearing fault identification method based on the AlexNet-Adaboost algorithm

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (2) : 20-25.

TANG Guiji，TIAN Yinchu，TIAN Tian

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Abstract

In view of the fact that the recognition rate of the traditional fault diagnosis method of rolling bearing is low under multiple working conditions in practical engineering. In this paper, a rolling bearing fault identification method based on Alexnet-Adaboost was proposed. Taking the time-frequency diagram of the rolling bearing signal as the model input and the classification result as the model output, several Alexnet-based classifiers were trained; on this basis, the Adaboost (adaptive lifting) algorithm was used to further improve the strong classifier, and the time-frequency diagram of the rolling bearing signal under multiple working conditions was input into the strong classifier for testing. The results show that the proposed method can effectively identify rolling bearing faults under multiple working conditions, and improves the fault classification accuracy to a certain extent.

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Fault diagnosis / time-frequency diagram / rolling bearing / convolutional neural network / Adaboost

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TANG Guiji，TIAN Yinchu，TIAN Tian. Multi-working condition rolling bearing fault identification method based on the AlexNet-Adaboost algorithm[J]. Journal of Vibration and Shock, 2022, 41(2): 20-25

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