针对齿轮传动疲劳寿命与可靠性评估中，小样本数据制约预测精度的核心瓶颈，本文突破传统方法的局限，构建了一种融合物理机理与试验数据的深度神经网络模型，旨在实现高置信度的概率寿命预测。该模型将应力-寿命物理约束嵌入网络架构中，构建了数据驱动与物理规律融合的联合优化目标，从而在小样本条件下实现了多应力水平齿轮疲劳寿命的精准预测。该方法有效克服了传统P-S-N曲线依赖大量试验、成本高昂且外推精度不足的局限，为齿轮传动系统的可靠性评估提供了一种兼具物理可解释性与工程实用性的概率寿命预测框架，显著提升了小样本场景下系统可靠性评估的准确度与效率。

To address the core bottleneck that the prediction accuracy in gear transmission fatigue life and reliability assessment is constrained by small-sample data, this study overcomes the limitations of conventional approaches by developing a deep neural network model that integrates physical mechanisms with experimental data, aiming to achieve high-confidence probabilistic life prediction. This model embeds stress-life (S-N) physical constraints into the network architecture and establishes a joint optimization objective integrating data-driven approaches and physical laws, thereby achieving accurate prediction of gear fatigue life under multiple stress levels under small-sample conditions. This method effectively overcomes the limitations of traditional P-S-N curves, which rely on extensive tests, involve high costs, and suffer from insufficient extrapolation accuracy. It provides a probabilistic life prediction framework with both physical interpretability and engineering practicality for the reliability assessment of gear transmission systems, significantly improving the accuracy and efficiency of system reliability assessment in small-sample scenarios.