A new convolutional dual-channel Transformer network with time window concatenation for remaining useful life prediction of rolling bearings

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Deep learning has achieved numerous breakthroughs in bearing predicting remaining useful life (RUL). However, the current mainstream deep learning framework inevitably has flaws, including the disadvantage of the small receptive field, the difficulty of learning long-term dependencies and the singularity of feature extraction domains, etc. Given the challenges mentioned above, we propose a new convolutional dual-channel Transformer network (CDCT) for remaining useful life prediction of rolling bearings. In the CDCT, the causal convolution operation is applied to extract local features from the time and frequency domains and add positional encoding to the input signal, while the transformer block is utilized for extracting bidirectional features and fusing them. The CDCT not only has a global receptive field but also can learn long-term dependencies regardless of sequence length. Besides, the time window concatenation is adopted to ameliorate the problem of large amounts of trainable parameters of the Transformer-based models. In the experiments, we conduct a detailed analysis of each crucial element and hyperparameter of the CDCT and compare it to multiple basic and advanced methods. The experimental results highlight the superiority of the CDCT in bearing RUL prediction and demonstrate the effectiveness of crucial elements in the CDCT.