MT-ConvFormer: A Multitask Bearing Fault Diagnosis Method Using a Combination of CNN and Transformer

In recent years, intelligent fault diagnosis methods for bearings, dominated by deep learning, have been widely applied. However, most current methods are still limited to diagnosing different types of faults or degrees of faults as single diagnostic targets. There is less research on methods that diagnose multiple fault tasks simultaneously, and there are difficulties in fully extracting complementary fault features, adapting to complex diagnostic environments with strong noise interference, and handling imbalanced data distribution across various working conditions. To overcome these difficulties and challenges, this article proposes an innovative multitask CNN-Transformer (MT-ConvFormer) bearing fault diagnosis method. This method consists of a shared layer responsible for learning globally shared information and several task-specific branch networks designed for different fault diagnosis tasks. First, the shared layer uses the CNN to extract local feature information and combines it with the Transformer to infer cyclic global dependencies of vibration signals, effectively mining globally shared information. Then, the task branch networks, by adopting an effective redundancy filtering mechanism, can more efficiently extract useful feature information from the shared layer and suppress the interference of irrelevant information. Experimental results show that this method exhibits significant diagnostic performance both under noisy conditions and under conditions of imbalanced data distribution across different working conditions. Moreover, comparative experiments with other existing methods further confirm the superiority of MT-ConvFormer under the same experimental setups.