The failure of rotating machinery can be prevented and eliminated by regular diagnosis of bearings. In the deep learning model of bearing fault diagnosis driven by big data, there often exist problems such as data acquisition difficulties, data distribution imbalance and high noise in samples. This study proposes a novel bearing fault diagnosis method using the joint feature extraction of Transformer and ResNet coupled with transfer learning strategy (TL-TAR) to overcome the abovementioned issues. First, the data is transmitted to the Transformer encoder and ResNet architecture respectively, where the input obtained by the encoder needs to separate features and word embedding through one-dimensional convolutional layer. Next, the feature sequences mined using encoder and ResNet are connected and classified. Moreover, the transfer learning strategy with model fine-tuning is exploited to reduce the train difficulty of the proposed method in new tasks. Experiments on two bearing fault datasets show that the proposed method can effectively combine the characteristics of both architectures, and the prediction accuracy outperform traditional deep learning networks in high noise environments.
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