基于声发射和数值模型的球轴承点蚀疲劳寿命估计研究

疲劳点蚀是滚动轴承失效的重要形式，传统的寿命计算方法离散度太大，缺乏对个体的指导意义。课题以球轴承为对象，针对点蚀形成之前的疲劳早期阶段，研究基于损伤建模和声发射的个体疲劳寿命估计方法，内容包括：①研究点蚀形成过程中微弱声发射信号的获取、降噪方法和多源信号分离方法，提高信号质量，进而通过模态声发射理论、时频分析等方法揭示信号演化与疲劳进程的关系；②考虑微观材料非均匀性，构建涵盖"裂纹萌生-扩展-点蚀形成"的疲劳损伤演化和寿命估计数值模型，进而引入材料断裂的能量释放机制，根据传递路径和弹性波理论，提出疲劳损伤的声发射辐射仿真模型，并经实验修正完善；③以声发射辐射仿真模型为桥梁，建立实测声发射特征和疲劳损伤数值模型之间的联系，研究利用实测数据改善模型中参数的不确定性，提高疲劳寿命估计的置信度。最终在在线条件下对寿命估计方法进行验证。项目有助于深化声发射监测理论基础，完善轴承寿命评估的方法体系。

Pitting is one of the most common failure types of rolling bearing. The large distribution of traditional life estimation methods makes them unsuitable for individual bearing life prediction. This project is aim to improve the remaining life estimation of rolling contact interfaces before pitting by combining fatigue modeling and Acoustic Emission (AE). The content of this research includes: (1) Design a life time experimental setup of ball bearing and acquire AE signals during whole test phases; Process the signals to reduce noise and separate AE from different fatigue sites; Analyze and obtain the correlations between AE characteristics and states of fatigue development via feature extraction methods. (2) Model the dynamic progress of rolling fatigue evolution with computational model, which simulates the whole procedure of mechanical fatigue from crack initiation to pitting formed, and the fatigue life can be estimated; Introduce energy release to the life computational model, investigate signal transfer path and function; Model the AE radiation mechanism, then revised according to experimental results; (3) Bridge the computational fatigue life model and experimental AE characteristics by the AE radiation mechanism model. The difference between experimental AE and predicted AE from radiation mechanism then is used to correct the parameters of life model, and a better life estimation can be achieved. The method will be further verified by online AE condition monitoring of ball bearing fatigue experiments. The research is essential for building more comprehensive methodologies for remaining life prediction of ball bearing and AE detection of machinery equipment.