Fast fault diagnosis algorithm for rolling bearing based on transfer learning and deep residual network

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (3) : 154-164.

LIU Fei1, CHEN Renwen1, XING Kailing2, DING Shanshan1, ZHANG Maiyi1

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Abstract

Aiming at the shortcomings of the existing deep learning-based rolling bearing fault diagnosis algorithms that the training parameters are large, the training time is long, and a large number of training samples are required, a fast fault diagnosis algorithm (TL-ResNet) based on Transfer Learning (TL) and Deep Residual Network (ResNet) is proposed. First developed a method of converting vibration signals into three-channel image data by combining short-time Fourier transform (STFT) with pseudo-color processing; then the ResNet18 model trained on the ImageNet dataset is used as a pre-training model, and applied to the field of rolling bearing fault diagnosis through the method of transfer learning; finally, the method of small sample transfer is proposed for the fault diagnosis of rolling bearing under different working conditions. Experiments were performed on the Case Western Reserve University（CWRU）and Universität Paderborn（PU） datasets. The diagnosis accuracy of TL-ResNet is 99.8% and 95.2%, respectively, and the training time of TL-ResNet on the CWRU dataset is only 1.5s, which shows that this algorithm is superior to other deep learning-based fault diagnosis algorithms and classic algorithms, it can be used for rapid fault diagnosis in actual industrial environments.

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Transfer learning / Deep learning / short-time Fourier transform (STFT) / deep residual network (ResNet) / Rolling bearing fault diagnosis

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LIU Fei1, CHEN Renwen1, XING Kailing2, DING Shanshan1, ZHANG Maiyi1. Fast fault diagnosis algorithm for rolling bearing based on transfer learning and deep residual network[J]. Journal of Vibration and Shock, 2022, 41(3): 154-164

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