融合TCN与双层LSTM的Transformer模型，提升工业设备剩余寿命预测精度。
Temporal convolutional and fusional transformer model with Bi-LSTM encoder-decoder for multi-time-window remaining useful life prediction
- 用TCN提取局部时序特征，结合改进的TFT与双向LSTM编码解码器
- 在多个时间窗口下测试，平均RMSE降低5.5%优于现有方法
- 适合需要高精度故障预警的工业场景，如智能制造和设备维护
健康预测对保障工业系统可靠性、减少停机时间和优化维护至关重要。剩余使用寿命（RUL）预测是该过程的核心环节，但现有模型难以同时捕捉细粒度时序依赖，并动态聚焦关键特征以实现稳健预测。为此，本文提出一种新框架：结合局部时序特征提取的时序卷积网络（TCNs）与增强型双向LSTM编码解码结构的时序融合变压器（TFT）。该架构有效连接短程与长程依赖关系，突出显著时序模式。此外，多时间窗口方法增强了不同工况下的适应性。在基准数据集上的广泛评估表明，所提模型平均RMSE降低最多达5.5%，显著优于当前先进方法。该框架填补了现有方法的关键空白，提升了工业预测系统的有效性，展现了先进时序变压器在RUL预测中的潜力。
原文摘要 · Abstract (English)
Health prediction is crucial for ensuring reliability, minimizing downtime, and optimizing maintenance in industrial systems. Remaining Useful Life (RUL) prediction is a key component of this process; however, many existing models struggle to capture fine-grained temporal dependencies while dynamically prioritizing critical features across time for robust prognostics. To address these challenges, we propose a novel framework that integrates Temporal Convolutional Networks (TCNs) for localized temporal feature extraction with a modified Temporal Fusion Transformer (TFT) enhanced by Bi-LSTM encoder-decoder. This architecture effectively bridges short- and long-term dependencies while emphasizing salient temporal patterns. Furthermore, the incorporation of a multi-time-window methodology improves adaptability across diverse operating conditions. Extensive evaluations on benchmark datasets demonstrate that the proposed model reduces the average RMSE by up to 5.5%, underscoring its improved predictive accuracy compared to state-of-the-art methods. By closing critical gaps in current approaches, this framework advances the effectiveness of industrial prognostic systems and highlights the potential of advanced time-series transformers for RUL prediction.
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