Remaining useful life estimation in aeroengines via regime-aware transformer architecture

Creative Commons License

Kaya G., KARACAN H.

JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, cilt.29, sa.4, 2026 (ESCI)

Özet

Remaining Useful Life (RUL) estimation has gained significant importance in recent years to improve the reliability of industrial systems. Within the scope of Predictive Maintenance (PdM), RUL estimation plays a critical role in supporting maintenance decision-making processes and planning maintenance activities more effectively. The integration of PdM methods is also needed in the aviation sector. However, the fact that aircraft engines operate under variable conditions, such as different altitudes and speeds, rather than constant conditions, makes the interpretation of sensor measurements difficult. This negatively affects RUL estimation performance. In this study, a regime-aware architecture called ReACT is proposed for aircraft engines operating under different conditions. The proposed architecture consists of two main stages. In the first stage, operating regimes are determined using the K-means clustering method, and regime-aware normalization is applied to the data. In the second stage, a Transformer architecture combining a two-stage attention mechanism with the Efficient Channel Attention mechanism is used. This structure effectively models temporal degradation patterns and relationships between sensors. Experimental results on the NASA C-MAPSS dataset show that the ReACT architecture significantly reduces remaining useful life estimation errors and provides significant improvements in RMSE and NASA Score metrics compared to existing methods.