Authors
Mahika Annie Verghese, C Christopher Columbus, E Elakiya
Published in
Scientific reports. Jul 12, 2026. Epub Jul 12, 2026.
Abstract
Accurate prediction of Remaining Useful Life (RUL) is essential for predictive maintenance in the aerospace industry, where unexpected failures pose significant safety risks and increase operational costs. Conventional deep learning models, such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), have demonstrated strong predictive capabilities; however, they often incur high computational costs, are sensitive to noise, and struggle to capture long-term degradation patterns. To overcome these issues, this study presents a new hybrid deep learning model that combines Temporal Convolutional Networks (TCNs) with Reservoir Computing, leveraging the strengths of both architectures. The model is tested using the well-known NASA C-MAPSS dataset, a standard benchmark for RUL estimation. Performance is measured using both Root Mean Squared Error (RMSE) and a penalty-based PHM score that emphasizes timely failure prediction. The model attains test RMSE values of 14.81, 16.26, 15.57, and 17.97 on FD001, FD002, FD003, and FD004, respectively. Correspondingly, the PHM scores are reduced to 57.1, 204.46, 190.95, and 446.73 across the same subsets. Experimental results demonstrate that the hybrid architecture consistently outperforms the standalone TCN and Reservoir components, as well as other benchmark methods, achieving substantially improved PHM scores while retaining competitive RMSE performance. These results suggest that the proposed method provides a practical, real-time solution for predictive maintenance in aeroengine health monitoring, thereby improving reliability and reducing maintenance costs.
PMID:
42437786
Bibliographic data and abstract were imported from PubMed on 13 Jul 2026.
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