航空燃油齿轮泵滑动轴承副的磨损失效是导致齿轮泵故障的主导因素，传统的滑动轴承寿命预测方法依赖大量试验数据，存在成本高昂、试验条件苛刻的局限性。提出了一种融合润滑磨损机理模型与主动学习的全载荷失效寿命评估方法，考虑了温度、弹性形变、粗糙表面影响，与加工和服役中的不确定性，构建了滑动轴承润滑磨损仿真模型，以表征轴承润滑特性与动态磨损行为；采用结合主动学习方法优化累积概率函数的学习预测过程，以显著降低样本需求，实现全载荷工况下失效概率与寿命分布的高效评估；通过矩独立敏感度分析给出了各不确定性因素对寿命的影响机制。研究表明：随着磨损深度的增加，磨损深度与轴承润滑特性显著关联，可以作为衡量滑动轴承性能退化程度的失效判据。主动学习方法所获取的轴承寿命分布显示，滑动轴承的失效主要发生在工作约687.88 h和859.60 h处，且其总寿命不会超过900 h。此外，分析敏感度发现轴承间隙随机公差的敏感度为0.998 201，间隙随机公差对轴承寿命的影响大于泵出口随机压力脉动。所提供的机理模型可用于燃油齿轮泵寿命预测与结构优化，能为航空燃油齿轮泵长寿命与高可靠性设计提供理论支持与工程指导。

The lubrication and wear failure of the sliding bearing pair in aviation fuel gear pumps is the main factor leading to gear pump failures. Traditional sliding bearing life prediction methods rely on a large amount of experimental data and have limitations such as high cost and harsh testing conditions. This article proposes a full load failure life assessment method that integrates lubrication and wear mechanism models with active learning. It comprehensively considers the effects of temperature， elastic deformation， and rough surfaces， and combines uncertainty in production and service to construct a sliding bearing lubrication and wear simulation model. The lubrication characteristics and dynamic wear behavior are characterized， and the learning and prediction process of the cumulative probability function is optimized using active learning methods， significantly reducing sample requirements and achieving efficient evaluation of failure probability and life distribution under full load conditions. In addition， the impact of various uncertainty factors on lifespan was analyzed through moment independent sensitivity analysis. Research has shown that with the increase of wear， various performance indicators of sliding bearings have deteriorated， proving the rationality of using wear as a failure criterion. On this basis， the bearing life distribution obtained through active learning methods shows that the failure of sliding bearings mainly occurs at approximately 687.88 h and 859.60 h of operation， and their total life will not exceed 900 h. In addition， sensitivity analysis revealed that the sensitivity of the random tolerance of the bearing clearance was 0.998 201， which is greater than the random pressure pulsation at the pump outlet. The service life of the sliding bearing is mainly affected by the random tolerance of the bearing clearance. The method proposed in this article for predicting and evaluating the life of sliding bearings in aviation fuel gear pumps based on lubrication and wear mechanism models will provide structural optimization efficiency for friction pairs， providing theoretical support and engineering guidance for the long-term and high reliability design of aviation fuel gear pumps.