FTT-GRU：一种用于剩余使用寿命预测的基于GRU的混合快速时间Transformer
FTT-GRU: A Hybrid Fast Temporal Transformer with GRU for Remaining Useful Life Prediction
AI总结：
本文提出混合模型FTT-GRU，结合快速时间Transformer与门控循环单元，在CMAPSS数据集上同时捕获全局与局部退化模式，实现了准确且高效的工业机械剩余使用寿命预测。
摘要：
准确预测工业机械的剩余使用寿命（RUL）对于减少停机时间和优化维护计划至关重要。现有方法，如长短期记忆（LSTM）网络和卷积神经网络（CNN），通常难以在多变量传感器数据中同时建模全局时间依赖性和细粒度退化趋势。我们提出了一种混合模型FTT-GRU，它将快速时间Transformer（FTT）——一种通过快速傅里叶变换（FFT）使用线性化注意力的轻量级Transformer变体——与门控循环单元（GRU）层相结合以进行序列建模。据我们所知，这是首次将FTT与GRU结合应用于NASA CMAPSS数据集的RUL预测，从而能够在紧凑的架构中同时捕获全局和局部退化模式。在CMAPSS FD001上，FTT-GRU实现了30.76的RMSE、18.97的MAE和R^2=0.45，在batch=1时的CPU延迟为1.12 ms。相对于已发表的最佳深度基准（TCN--Attention），它将RMSE提高了1.16%，MAE提高了4.00%。在k=3次运行的平均训练曲线上显示出平滑的收敛和狭窄的95%置信区间，并且消融实验（仅GRU、仅FTT）支持了这两个组件的贡献。这些结果表明，紧凑的Transformer-RNN混合模型在CMAPSS上提供了准确且高效的RUL预测，使其适用于实时工业预测。
英文摘要：
Accurate prediction of the remaining useful life (RUL) of industrial machinery is essential for reducing downtime and optimizing maintenance schedules. Existing approaches, such as long short-term memory (LSTM) networks and convolutional neural networks (CNNs), often struggle to model both global temporal dependencies and fine-grained degradation trends in multivariate sensor data. We propose a hybrid model, FTT-GRU, which combines a Fast Temporal Transformer (FTT) -- a lightweight Transformer variant using linearized attention via fast Fourier transform (FFT) -- with a gated recurrent unit (GRU) layer for sequential modeling. To the best of our knowledge, this is the first application of an FTT with a GRU for RUL prediction on NASA CMAPSS, enabling simultaneous capture of global and local degradation patterns in a compact architecture. On CMAPSS FD001, FTT-GRU attains RMSE 30.76, MAE 18.97, and $R^2=0.45$, with 1.12 ms CPU latency at batch=1. Relative to the best published deep baseline (TCN--Attention), it improves RMSE by 1.16\% and MAE by 4.00\%. Training curves averaged over $k=3$ runs show smooth convergence with narrow 95\% confidence bands, and ablations (GRU-only, FTT-only) support the contribution of both components. These results demonstrate that a compact Transformer-RNN hybrid delivers accurate and efficient RUL predictions on CMAPSS, making it suitable for real-time industrial prognostics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
