CART-net: A Causal Adaptive Residual Time Network for Remaining Useful Life Prediction of Aero-Engines Under Varying Operating Conditions

Aero-engines operate in complex environments, where accurate prediction of their remaining useful life (RUL) is critical to flight safety and operational efficiency. The rapid development of the Internet of Things (IoT) enables more accurate real-time monitoring of engines, providing valuable sensor data for RUL prediction. However, existing studies have largely overlooked the impact of frequently varying operating conditions on degradation patterns. To address this issue, this paper proposes an innovative prediction framework termed the Causal Adaptive Residual Time Network (CART-net). The framework first treats operating conditions as causal modulating factors, introducing Conditional Adaptive Dynamic Feature Enhancement (CADFE) and the Causal Adaptive Attention Mechanism (CAAM). CADFE effectively decouples the influence of operating conditions by dynamically weighting sensor data, while CAAM integrates condition-guided strategies with causal masking to enhance temporal consistency and capture dynamic correlations in the degradation process. Subsequently, a Dynamic Temporal Convolutional Network is employed, leveraging a multi-layer causal convolutional structure, dynamic temporal embedding, and adaptive residual connections to effectively capture long-term dependency patterns and nonlinear temporal evolution characteristics during degradation. Experimental results on the CMAPSS and N-CMAPSS datasets demonstrate that CART-net significantly outperforms existing mainstream methods in key metrics such as RMSE and Score, offering a novel theoretic perspective and technical approach for RUL prediction of aero-engines under varying operating conditions.