Degradation behavior judgment-enabled improved transferable squeeze aggregated excitation remaining useful life prediction network for rolling bearing

Abstract Remaining useful life prediction of rolling bearings in the various operating conditions is of significance for improving the reliability and durability of rotating machinery. However, the precision of RUL prediction is heavily affected by the significant differences in data feature distributions due to the different failure degradation behaviors of bearings and the various operating conditions. To address this issue, a novelty RUL prediction method based on degradation behavior judgment is proposed. Firstly, a method is proposed to judge the failure behavior, the deep convolutional autoencoder is used to construct the prior gradient health indicator and current health indicators of rolling bearings, and dynamic time warping is used to measure the difference between the two to judge failure degradation behavior. Then, an improved squeeze aggregated excitation network is proposed for RUL prediction. The squeeze aggregated excitation module, which embeds the residual shrinkage denoise module, is used to learn the mapping relationship between features and RUL to obtain comprehensive information. The maximum mean discrepancy is introduced into the final layer of the improved squeeze aggregated excitation network to reduce feature distribution differences. Results of the experiment show that this model has higher prediction precision and better robustness than other methods under three working conditions in the PHM 2012 dataset.