A Transformer-Based framework with historical data fusion for RUL prediction

Accurate prediction of the remaining useful life (RUL) of industrial equipment is critical to operational safety and efficiency, particularly in complex multi-condition environments. However, existing methods often struggle to capture degradation patterns effectively due to the use of isolated time-series sampling strategies. To address this limitation, this study proposes a novel RUL prediction framework based on a window-fused data construction strategy, enhancing degradation modeling through systematically integrating historical operational data. A multi-layer sliding window approach is first employed to construct temporal samples, enabling alignment of operational phases and capturing cross-stage dependencies. Principal Component Analysis (PCA) is then used to fuse window-level features, thereby mitigating redundancy and noise while retaining key information about degradation. Finally, the PCA-fused window-level features are fed into the MDCCB-ISEformer model for RUL prediction. This hybrid deep learning model combines a Multi-Dilated Causal Convolution Block (MDCCB) for extracting multi-scale temporal features with ISEformer, an improved Transformer architecture incorporating adaptive multi-head weighting inspired by the Squeeze-and-Excitation (SE) mechanism. This design allows the model to dynamically recalibrate the importance of individual attention heads, thereby improving its ability to model complex degradation trajectories and temporal dependencies. Experimental results on benchmark datasets, including C-MAPSS and a milling tool wear dataset, validate the superior performance of the proposed framework, demonstrating its impressive diagnosis capability across diverse industrial scenarios.