Abstract: To address the scarcity of abnormal samples and the challenges in operational reliability modeling for civil aircraft, this paper proposes an operational reliability assessment method based on a mechanism-enhanced conditional generative adversarial network(ME-CGAN). Within the ME-CGAN framework, CGAN is employed to generate failure data samples. System failure mechanisms are analyzed to construct a fault logic diagram, which associates faults with Quick Access Recorder (QAR) parameters. A multi-layer perceptron is then utilized to establish a logical verification model for operational data. This logical verification model is placed after the CGAN discriminator to perform anomaly validation on the generated samples using fault logic, while also providing a new backpropagation mechanism for network hyperparameter optimization. The engineering applicability of the ME-CGAN method is demonstrated through two case studies involving the LG lever disagree and HYD 1 ACMP fail. Moreover, the modeling and simulation performance of the ME-CGAN method is evaluated through comparisons with various mathematical approaches. Experimental results indicate that the ME-CGAN method achieves high efficiency in generating failure samples and can effectively enhance the accuracy of operational reliability modeling and solution processes for civil aircraft.

Key words: civil aircraft, fault logic graph, fail mechanism, conditional generative adversarial, operational reliability