Aeroengine operation reliability (AOR) estimation is important for stakeholders in operating, monitoring, designing, and improving. The Quick Access Recorder (QAR) flight data and failure rate of aeroengine are uti-lized to analyze AOR. Considering the uncertainty in AOR assessment, a Bayesian neural network (BNN) is trained to evaluate and forecast AOR based on aeroengine status data within a confidence interval. Further, to quantify the degree of each feature on AOR, Shapley Additive ex-Planations (SHAP) values are calculated based on the Light gradient boosting machine (LightGBM) to study the degree and direction of influence feature on AOR. In this study, it is revealed that (i) AOR is closely related to the airplane flight stages; and (ii) after training with eight flights and validation with two flights data from QAR data, BNN can achieve AOR analysis and prediction within a certain confidence interval while obtaining aeroengine state data; and (iii) the feature importance and influence direction are quantified by SHAP values, it demonstrates the sensitive factors in AOR analysis. Based QAR data, this study provide an AOR analysis framework to improve the operation and design, which has the potential to support aeroengine real-time status monitoring and health management.
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