  In modern industrial production, the prediction ability of the remaining
useful life (RUL) of bearings directly affects the safety and stability of the
system. Traditional methods require rigorous physical modeling and perform
poorly for complex systems. In this paper, an end-to-end RUL prediction method
is proposed, which uses short-time Fourier transform (STFT) as preprocessing.
Considering the time correlation of signal sequences, a long and short-term
memory network is designed in CNN, incorporating the convolutional block
attention module, and understanding the decision-making process of the network
from the interpretability level. Experiments were carried out on the 2012PHM
dataset and compared with other methods, and the results proved the
effectiveness of the method.
