摘 要
机械部件的疲劳寿命预测与可靠性分析是现代工程领域的重要课题，其研究背景源于工业设备在复杂工况下的长期运行需求。随着机械设备向高负荷、高效率方向发展，疲劳失效问题日益突出，亟需建立精确的预测模型以优化设计和维护策略。本研究旨在通过综合考虑材料特性、载荷条件及环境因素，提出一种基于多源数据融合的疲劳寿命预测方法，并结合概率统计理论实现对机械部件可靠性的量化评估。具体而言，采用有限元分析与实验测试相结合的方式获取应力-应变分布特征，同时引入机器学习算法对历史失效数据进行挖掘，构建了能够反映实际服役条件的疲劳损伤演化模型。结果表明，所提方法在不同工况下均表现出较高的预测精度，且相较于传统方法显著提升了计算效率。此外，通过引入不确定性分析框架，进一步明确了关键参数对系统可靠性的影响规律。本研究的主要创新点在于将数据驱动与物理机制深度融合，为复杂环境下机械部件的寿命管理提供了新思路，其成果可广泛应用于航空航天、交通运输等领域，具有重要的理论价值和工程意义。
关键词：疲劳寿命预测；多源数据融合；可靠性分析
Abstract: The prediction of fatigue life and reliability analysis of mechanical components is a critical topic in modern engineering, driven by the need for industrial equipment to operate under complex conditions over extended periods. As mechanical systems evolve towards higher loads and greater efficiency, fatigue failure has become increasingly prominent, necessitating the development of accurate predictive models to optimize design and maintenance strategies. This study proposes a fatigue life prediction method based on multi-source data fusion, comprehensively considering material properties, loading conditions, and environmental factors, while integrating probabilistic statistical theory to quantitatively assess the reliability of mechanical components. Specifically, stress-strain distribution characteristics are obtained through a combination of finite element analysis and experimental testing, and machine learning algorithms are employed to mine historical failure data, thereby constructing a fatigue damage evolution model that reflects actual service conditions. The results demonstrate that the proposed method achieves high prediction accuracy across various operating conditions and significantly improves computational efficiency compared to traditional approaches. Furthermore, by incorporating an uncertainty analysis fr amework, the influence patterns of key parameters on system reliability are clarified. A major innovation of this research lies in the deep integration of data-driven methods with physical mechanisms, offering new insights into the life management of mechanical components under complex environments. The findings have broad applicability in fields such as aerospace and transportation, holding significant theoretical value and practical engineering implications.
Keywords: Fatigue Life Prediction; Multi-Source Data Fusion; Reliability Analysis
目 录
1绪论 1
1.1机械部件疲劳寿命预测的研究背景 1
1.2疲劳寿命预测与可靠性分析的意义 1
1.3国内外研究现状综述 2
1.4本文研究方法与技术路线 2
2疲劳寿命预测的理论基础 2
2.1疲劳损伤的基本原理 2
2.2材料疲劳特性的表征方法 3
2.3应力-寿命模型及其应用 3
2.4应变-寿命模型的构建与验证 4
2.5环境因素对疲劳寿命的影响 4
3可靠性分析的关键技术 5
3.1可靠性分析的基本概念与框架 5
3.2概率统计在可靠性评估中的应用 5
3.3基于失效数据的可靠性建模 6
3.4不确定性对可靠性分析的影响 6
3.5高置信度小样本问题的解决方法 7
4疲劳寿命预测与可靠性分析的综合研究 7
4.1数据驱动的疲劳寿命预测方法 7
4.2基于有限元的应力场分析 8
4.3疲劳寿命与可靠性的耦合关系 9
4.4实验验证与结果分析 9
4.5工程案例分析与优化建议 10
结论 11
参考文献 12
致 谢 13
