This paper reports a reduced-order modeling framework of bladed disks on a rotating shaft to simulate the vibration signature of faults like cracks in different components aiming towards simulated data-driven machine learning. We have employed lumped and one-dimensional analytical models of the subcomponents for better insight into the complex dynamic response. The framework seeks to address some of the challenges encountered in analyzing and optimizing fault detection and identification schemes for health monitoring of rotating turbomachinery, including aero-engines. We model the bladed disks and shafts by combining lumped elements and one-dimensional finite elements, leading to a coupled system. The simulation results are in good agreement with previously published data. We model the cracks in a blade analytically with their effective reduced stiffness approximation. Multiple types of faults are modeled, including cracks in the blades of single and two-stage bladed disks, Fan Blade Off (FBO), and Foreign Object Damage (FOD). We have applied aero-engine operational loading conditions to simulate realistic scenarios of online health monitoring. The proposed reduced-order simulation framework will have applications in probabilistic signal modeling, machine learning toward fault signature identification, and parameter estimation with measured vibration signals.

中文翻译：

---

一种用于模拟叶盘故障特征的降阶建模框架

本文报告了一种旋转轴上叶盘的降阶建模框架，以模拟不同部件中裂纹等故障的振动特征，旨在模拟数据驱动的机器学习。我们采用了子组件的集总和一维分析模型，以便更好地了解复杂的动态响应。该框架旨在解决在分析和优化旋转涡轮机械（包括航空发动机）健康监测的故障检测和识别方案时遇到的一些挑战。我们通过组合集总元和一维有限元来模拟叶盘和轴，从而形成耦合系统。模拟结果与先前公布的数据非常吻合。我们使用有效降低刚度近似值对叶片中的裂纹进行分析建模。对多种类型的故障进行建模，包括单级和两级叶盘的叶片裂纹、风扇叶片脱落 (FBO) 和异物损坏 (FOD)。我们已经应用航空发动机运行负载条件来模拟在线健康监测的真实场景。所提出的降阶仿真框架将应用于概率信号建模、故障特征识别的机器学习以及测量振动信号的参数估计。我们已经应用航空发动机运行负载条件来模拟在线健康监测的真实场景。所提出的降阶仿真框架将应用于概率信号建模、故障特征识别的机器学习以及测量振动信号的参数估计。我们已经应用航空发动机运行负载条件来模拟在线健康监测的真实场景。所提出的降阶仿真框架将应用于概率信号建模、故障特征识别的机器学习以及测量振动信号的参数估计。