A Time-Delay Low-Rank Reconstruction and Attention-BP-LSTM Framework for Axle-Box Bearing Temperature Prediction in Railway Vehicles

The accuracy of axle-box bearing temperature prediction is important for monitoring the operating condition of high-speed electric multiple units. However, temperature data collected in service often contain missing values, abnormal fluctuations, and noise, which can affect the reliability of the prediction results. To address this problem, this paper proposes an Attention-BP-LSTM framework that combines data preprocessing with temperature prediction. First, missing data are completed by linear interpolation. The dynamically augmented temperature sequence is then processed using time-delay low-rank sparse decomposition to separate the normal temperature variation trend, sparse anomalies, and measurement noise, based on which the abnormal data are reconstructed. After Z-score normalization, the processed time series is converted into supervised learning samples using a sliding window. The prediction model consists of LSTM, an attention mechanism, Dropout, and a BP network. LSTM is used to extract the temporal dependencies in the temperature sequence, the attention mechanism assigns corresponding weights to different historical features, Dropout alleviates model overfitting, and the BP network completes the nonlinear mapping from temporal features to the predicted temperature. Experimental results based on axle-box bearing temperature data show that, compared with BP, LSTM, and BP-LSTM, the proposed model achieves lower MAE and RMSE and a higher R2. This indicates that improving data quality before prediction, together with attention-based temporal feature extraction, helps improve the reliability of short-term axle-box bearing temperature prediction.