ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Integrated mechanism and generative adversarial surrogate modeling for aircraft systems reliability evaluation
Received date: 2024-07-15
Revised date: 2024-09-09
Accepted date: 2024-10-08
Online published: 2024-10-15
Supported by
National Natural Science Foundation of China(51875465)
To effectively perform aircraft system reliability evaluation， the Mechanism and Data dual-drive Reliability Monitoring （MDRM） concept has been proposed. In the MDRM concept， the fault logic diagram is constructed from the perspective of forward mechanism based on the Functional Hazard Analysis （FHA）， Failure Mode and Effects Analysis （FMEA）， and Fault Tree Analysis （FTA）. By integrating optional data， a Bayesian network model is built to select important influencing parameters. The generative adversarial theory is introduced into the surrogate model， and a generative adversarial surrogate modeling strategy is presented to establish a correlation model between the influencing parameters and research object， thus enabling the reliability evaluation. The Generative Adversarial Regression Network （GARN） method is proposed for aircraft systems reliability evaluation by integrating the MDRM concept with neural network models and compact support region thought. In addition， the mathematical cases are adopted to demonstrate the modeling performance of proposed GARN， and the engineering applicability of developed method are verified through the No.1 hydraulic system low-pressure and landing gear brake temperature multi-failures of a domestic civil aircraft. The comparison of several methods shows that GARN holds outstanding modeling and simulation performance advantages， and the proposed concept and method can provide strong theoretical and technical support for aircraft system reliability assessment.
Yunwen FENG , Da TENG , Cheng LU , Rui WANG , Junyu CHEN . Integrated mechanism and generative adversarial surrogate modeling for aircraft systems reliability evaluation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(7) : 230948 -230948 . DOI: 10.7527/S1000-6893.2024.30948
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