{"ID":2847275,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00564","arxiv_id":"2511.00564","title":"FTT-GRU: A Hybrid Fast Temporal Transformer with GRU for Remaining Useful Life Prediction","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2511.00564","url_pdf":"https://arxiv.org/pdf/2511.00564v1","authors":"[\"Varun Teja Chirukiri\",\"Udaya Bhasker Cheerala\",\"Sandeep Kanta\",\"Abdul Karim\",\"Praveen Damacharla\"]","published":"2025-11-01T14:02:03Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"eess.SY\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}