Aero-Engine Health State Prediction Using EnsembleBRB-based SHapley Additive exPlanations Approach

As an important aspect of engine health management, predicting the health state of aero-engines can provide quantitative basis for improving aircraft reliability and reducing engine maintenance costs. However, traditional aviation engine health state prediction methods lack sufficient attention to interpretability, resulting in a decreased support for engine maintenance decision-making, specifically when condition-dependent. This study proposes an interpretable prediction method for the health state of aero-engines based on the EnsembleBRB-SHAP approach, considering the demand for interpretability in engine health state prediction. First, a data-driven approach is used to train multiple sub-aero-engine health state prediction models based on a Belief Rule Base (BRB). Subsequently, an EnsembleBRB model is constructed for predicting the health state of aero-engines such that it utilizes multiple sources of uncertain data while ensuring prediction accuracy. Based on the SHapley Additive exPlanations (SHAP) framework, the constructed EnsembleBRB model is analyzed and interpreted to identify key features and achieve an interpretable prediction of the aero-engine health state. Finally, the feasibility and effectiveness of the proposed method are verified by introducing experimental monitoring data of engine faults recorded using the Commercial Modular Aero-Propulsion System Simulation software. Experimental results show that the Mean Square Error (MSE) of the proposed method in predicting the health status of aero-engines is 0.012 2. By analyzing local and global interpretability, the Low-Pressure Turbine (LPT) coolant bleed and physical fan speed are identified as the key parameters determining engine health status, which in turn can better support decision-making for managing aero-engine health and other work.