Bearing Lifespan Reliability Prediction Method Based on Multiscale Feature Extraction and Dual Attention Mechanism

Accurate prediction of the remaining useful life (RUL) of rolling bearings was crucial for ensuring the safe operation of machinery and reducing maintenance losses. However, due to the high nonlinearity and complexity of mechanical systems, traditional methods failed to meet the requirements of medium- and long-term prediction tasks. To address this issue, this paper proposed a recurrent neural network with a dual attention model. By employing path weight selection methods, Discrete Fourier transform, and selection mechanisms, the prediction accuracy and generalization ability in complex time series analysis were significantly improved. Evaluation results based on mean absolute error (MAE) and root mean square error (RMSE) indicated that the dual attention mechanism effectively focused on key features, optimized feature extraction, and improved prediction performance. An end-to-end RUL prediction model was established based on the MS-DAN network, and the effectiveness of the method was validated using the IEEE PHM 2012 Data Challenge dataset, providing more accurate decision support for equipment maintenance engineers.