Abstract:

Maintenance-interval prediction focuses on the proactive scheduling of equipment downtime, arranging maintenance before performance degradation reaches a predefined threshold while aligning with engineering operations. Accurate prediction of such intervals is vital for reliable equipment operation but remains challenging due to difficulties in multi-source data fusion, quantitative degradation characterization, and long-term dependency learning. This study presented a degradation-driven temporal modeling method that dynamically represented performance deterioration during continuous operation and adaptively captured complex dependencies among multi-sensor data. A performance-degradation indicator (PDI) quantified equipment performance decline using time-series measurements. To capture correlations among multi-source features, a sequence-to-sequence prediction model with multi-head attention was constructed and degradation-aware parameters were integrated, which optimized feature weighting and improved long-term trend prediction. The experimental results indicated that the optimal performance of the model improved by nearly 13.5% after integrating PDI. On the TBM (tunnel boring machine) engineering dataset, an RMSE (root mean square error) improvement of approximately 25% was achieved compared to the standard LSTM (long short-term memory), and outperformed other models by nearly 15%, yielding high prediction accuracy. Further evaluation on the C-MAPSS dataset against RNN (recurrent neural network), GNN (graph neural network), and attention-based methods confirmed the approach’s effectiveness, offering a detailed analysis of how varying the number of sensors affected model performance. The method also exhibited strong scalability and could be extended to incorporate environmental-condition awareness, providing technical support for intelligent maintenance decision-making and closed-loop operational control.

Key words: maintenance interval prediction, multi-head attention mechanism, sequence-to-sequence model, deep learning, intelligent maintenance systems