Operational reliability evaluation method for civil aircraft based on mechanism -enhanced conditional generative adversarial

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(M E-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(MLP)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 disagreement and HYD 1 ACMP failure. 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.