Abstract Rotor systems play a vital role in processing machinery, aero engines, and wind turbines. Since moving parts can be worn during operation, the system performance is variable throughout the service period actually, especially for the rotor system with various defects. Unfortunately, the existing models about bearing defects are established neglecting the changes of dynamic behaviors, which may not reflect the practice well. Thus, a model that considers this variation is proposed in this paper. The effects of both distributed and localized defects are modeled under the excitation from the system itself. Based on the Lankarani-Nikaravesh model and experimental observations, we established a time-varying wear model. The influences are considered not only on system dynamic behaviors but also on the geometry topographies of both parts and defects. The proposed model is validated with experimental data and prior researches. Extensive case studies are carried out to investigate the influences of the defects on system performance under an imbalanced excitation. The comparison performances of diverse systems at different times are conducted to illustrate the effects of the wear on systems responses. This model provides a better understanding for the performance evolution of rotor systems during the service period. Additionally, this model is also applicable for system fault diagnosis and health monitoring.

中文翻译：

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多轴承故障转子系统时变综合动力学模型

摘要 转子系统在加工机械、航空发动机和风力涡轮机中起着至关重要的作用。由于运动部件在运行过程中会发生磨损，因此系统性能实际上在整个使用期内是可变的，特别是对于存在各种缺陷的转子系统。遗憾的是，现有的轴承缺陷模型建立时忽略了动态行为的变化，可能不能很好地反映实践。因此，本文提出了一个考虑这种变化的模型。分布和局部缺陷的影响都是在系统本身的激励下建模的。基于Lankarani-Nikaravesh模型和实验观察，我们建立了时变磨损模型。不仅要考虑对系统动态行为的影响，还要考虑零件和缺陷的几何形貌。所提出的模型通过实验数据和先前的研究进行了验证。进行了广泛的案例研究，以研究在不平衡激励下缺陷对系统性能的影响。进行了不同系统在不同时间的比较性能，以说明磨损对系统响应的影响。该模型为转子系统在服务期间的性能演变提供了更好的理解。此外，该模型还适用于系统故障诊断和健康监测。进行了广泛的案例研究，以研究在不平衡激励下缺陷对系统性能的影响。进行了不同系统在不同时间的比较性能，以说明磨损对系统响应的影响。该模型为转子系统在服务期间的性能演变提供了更好的理解。此外，该模型还适用于系统故障诊断和健康监测。进行了广泛的案例研究，以研究在不平衡激励下缺陷对系统性能的影响。进行了不同系统在不同时间的比较性能，以说明磨损对系统响应的影响。该模型为转子系统在服务期间的性能演变提供了更好的理解。此外，该模型还适用于系统故障诊断和健康监测。