Remaining useful life prediction of rolling bearings based on multi-wavelet-time convolution transformer

Abstract Aiming at the problems of insufficient local information extraction ability of traditional Transformer and the difficulty of capturing temporal features from the degradation process in the remaining useful life (RUL) prediction, a novel rolling bearing RUL prediction method based on multi-wavelet-time convolution transformer is proposed. First, a temporal attention unit is designed to capture the relevant degradation state features at different moments, and a multiple wavelet convolution layer (MWCL) is also designed to acquire the time-frequency features from vibration signal. Then, in order to take into account the sensitivity between different degradation features in the process of feature fusion, cross-attention mechanism are employed to generate salient feature. Finally, to comprehensively capture the global and local degradation features from the extracted features, a multiscale convolutional transformer is constructed by combining the multiscale temporal convolutional network and the multi-head attention mechanism. Two sets of experiments are conducted on the PHM 2012 datasets to verify the rationality and effectiveness of the proposed algorithm, the results indicate that performance of our method is superior to the existing SOTA methods.