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

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.