Remaining useful life prediction of rolling bearings using a residual attention network with multi-scale feature extraction and temporal dependency enhancement

Predicting the remaining useful life (RUL) of rolling bearings is crucial for industrial machinery maintenance, non-destructive testing and evaluation (NDT). To address the challenges posed by noise interference and redundant information, this paper proposes a novel approach utilising residual attention networks and multi-scale feature extraction. The method enhances feature extraction by combining shallow and deep convolutional layers while employing bidirectional LSTM to capture both short-term and long-term dependencies in time series data. The incorporation of a residual attention fusion module further enhances the model's ability to focus on important features, ensuring more stable training and better prediction performance. After validation on PHM2012, IMS, and laboratory self-constructed datasets, the proposed model demonstrates superior performance compared to existing methods, significantly reducing prediction errors. The practical application of this work lies in its potential integration into industrial predictive maintenance systems, providing a solid foundation for expanding predictive maintenance strategies in complex real-world industrial environments.