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In modern industrial production, the prediction ability of the remaininguseful life (RUL) of bearings directly affects the safety and stability of thesystem. Traditional methods require rigorous physical modeling and performpoorly for complex systems. In this paper, an end-to-end RUL prediction methodis proposed, which uses short-time Fourier transform (STFT) as preprocessing.Considering the time correlation of signal sequences, a long and short-termmemory network is designed in CNN, incorporating the convolutional blockattention module, and understanding the decision-making process of the networkfrom the interpretability level. Experiments were carried out on the 2012PHMdataset and compared with other methods, and the results proved theeffectiveness of the method.

SAL-CNN: Estimate the Remaining Useful Life of Bearings Using Time-frequency Information