Multichannel Attention-Based TCN-GRU Network for Remaining Useful Life Prediction of Aero-Engines

Jiabao Zou and Ping Lin ()
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Jiabao Zou: School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
Ping Lin: School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China

Energies, 2025, vol. 18, issue 8, 1-15

Abstract: Predictive maintenance is a cornerstone of modern aerospace engineering, critical for maintaining the reliability and operational performance of aircraft engines. As a major component of prognostics and health management (PHM) technology, the accurate prediction of remaining useful life (RUL) enables proactive maintenance strategies, minimizes downtime, reduces costs, and enhances safety. This paper presents an innovative RUL prediction model designed specifically for aircraft engine applications. The model combines a temporal convolutional network (TCN) with multichannel attention and a gated recurrent unit (GRU) network. The framework begins with data pre-processing, followed by temporal feature extraction through an overlaying TCN network. Then, a multichannel attention mechanism fuses information from multiple TCN blocks, capturing rich feature representations. Finally, the fused data are processed by the GRU network to deliver precise RUL predictions. An improvement of at least 8.1% and 12.6% has been observed in two prediction metrics for the CMAPSS dataset when compared to other models.

Keywords: aero-engines; temporal convolutional network; attention mechanism; remaining useful life prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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