Rolling bearing fault diagnosis method based on MTF-CNN

LEI Chunli1,2, XIA Benfeng1,2, XUE Linlin1,2, JIAO Mengxuan1,2, ZHANG Huqiang1,2

Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (9) : 151-158.

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PDF(4386 KB)
Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (9) : 151-158.

Rolling bearing fault diagnosis method based on MTF-CNN

  • LEI Chunli1,2, XIA Benfeng1,2, XUE Linlin1,2, JIAO Mengxuan1,2, ZHANG Huqiang1,2
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Abstract

Aiming at the problem that the traditional fault diagnosis method has low recognition accuracy for bearing fault diagnosis when the actual working condition of rolling bearing is complex and changeable and the amount of datasets is small, a novel rolling bearing fault diagnosis model based on MTF-CNN is proposed. Firstly, the original one-dimensional vibration signal is transformed into two-dimensional feature image with time correlation by using Markov transition field (MTF) coding, and then the feature image is used as the input of convolutional neural network (CNN) for automatic feature extraction and fault diagnosis. Finally, the classification of different fault types is realized. In order to verify the effectiveness and superiority of the proposed method, the rolling bearing data of Case Western Reserve University is selected for experimental verification, and the generalization performance of the proposed method is tested when the load changes and the data set size is different, and compared with the traditional intelligent algorithm. The results show that, compared with other common fault diagnosis methods, the proposed model has better generalization performance and recognition effect for rolling bearing fault diagnosis in the environment of small amount of datasets and load change.

Key words

fault diagnosis / rolling bearing / markov transition field / convolution neural network

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LEI Chunli1,2, XIA Benfeng1,2, XUE Linlin1,2, JIAO Mengxuan1,2, ZHANG Huqiang1,2. Rolling bearing fault diagnosis method based on MTF-CNN[J]. Journal of Vibration and Shock, 2022, 41(9): 151-158

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