Bearing fault diagnosis based on double-graph conversion and fusion CRNNs

LI Zhe, KARI Tusongjiang, FAN Xiang, FAN Zhipeng, WAN Rongqi, BAI Xinyue, WU Yutong

Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (19) : 240-248.

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PDF(3560 KB)
Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (19) : 240-248.

Bearing fault diagnosis based on double-graph conversion and fusion CRNNs

  • LI Zhe, KARI Tusongjiang, FAN Xiang, FAN Zhipeng, WAN Rongqi, BAI Xinyue, WU Yutong
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Abstract

Aiming at the problems that one-dimensional vibration sequence input limits convolutional neural network performance, and the single data processing method restricts the deep mining of bearing fault characteristics under actual complex working conditions, a rolling bearing fault diagnosis method based on the fusion of double graph conversion and multiple convolutional recurrent neural network is proposed. Firstly, one-dimensional sequence signals are converted into two-dimensional images by using Gramian angular difference field and Markov transition field coding methods respectively. Then, the converted two modal images are simultaneously input into the Fu-CRNN network model of multi-CRNN fusion, fully absorbing the advantages of the two conversion methods and improving the feature expression ability of CRNN model. Finally, the adaptive feature extraction and end-to-end diagnosis of bearing signals are realized. To verify the reliability and superiority of this method, Case Western Reserve University rolling bearing data set is selected to set up bearing fault diagnosis experiments, and the diagnostic performance is compared with that of traditional methods. The results show that the recognition accuracy and generalization effect of the proposed model are better than those of single modal sample input model, and it is also better than other common algorithms, which can provide reference for sample construction and bearing fault diagnosis.

Key words

rolling bearing / fault diagnosis / Gramian angular difference field / Markov transition field / Fusion

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LI Zhe, KARI Tusongjiang, FAN Xiang, FAN Zhipeng, WAN Rongqi, BAI Xinyue, WU Yutong. Bearing fault diagnosis based on double-graph conversion and fusion CRNNs[J]. Journal of Vibration and Shock, 2023, 42(19): 240-248

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