为有效开展飞机系统可靠性评估，提出了一种机理与数据双驱的可靠性评估（Mechanism and data dual-drive reliability monitoring concept, MDRM）理念，在MDRM理念中，基于功能危险性分析（Functional hazard analysis, FHA）、故障模式及影响分析（Failure mode and effects analysis, FMEA）和故障树分析（Fault tree analysis, FTA），从正向机理角度构建故障逻辑图，结合运行数据构建贝叶斯网络模型选取重要影响参数，将生成对抗理论引入代理模型，提出了生成对抗代理建模策略，用于建立影响参数与研究对象的关联模型，进而实现可靠性评估。基于MDRM理念，融合神经网络模型和紧支撑域思想，提出了生成对抗回归网络（Generative adversarial regression network, GARN）方法用于飞机系统的可靠性评估。此外，运用数学案例证明所提出GARN的建模性能，通过某型国产民机1号液压系统低压和起落架刹车温度多失效案例验证所研究方法的工程适用性。通过多种方法对比表明，GARN具有突出的建模性能和仿真性能优势，所提出的理念和方法可为飞机系统可靠性评估提供有力的理论和技术支撑。

To effectively perform aircraft system reliability evaluation, the Mechanism and Data dual-drive Reliability Monitor-ing (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); the selection of important influencing parameters combined with operating data and Bayesian networks; 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, then the reliability evaluation is realized. The Generative Adversar-ial 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 temper-ature multi-failures of a domestic civil aircraft. The comparison of several methods shows that GARN holds out-standing modeling and simulation performance advantages, and the proposed concept and method can provide strong theoretical and technical support for aircraft system reliability assessment.