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Operational reliability evaluation method for civil aircraft based on mechanism-enhanced conditional generative adversarial
Received date: 2025-06-27
Revised date: 2025-07-31
Accepted date: 2025-08-20
Online published: 2025-08-28
Supported by
Fund of Shanghai Engineering Research Center of Civil Aircraft Health Monitoring(GCZX-2024-02)
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 （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.
Yunwen FENG , Wanyi LIU , Qianyun KE , Cheng LU , Rui WANG . Operational reliability evaluation method for civil aircraft based on mechanism-enhanced conditional generative adversarial[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2026 , 47(6) : 232483 -232483 . DOI: 10.7527/S1000-6893.2025.32483
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