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.
翻译:本文报告旋转轴上的刀片磁盘缩放模型框架,以模拟断层的振动信号,如不同组成部分的裂缝,目的是模拟数据驱动机的模拟学习;我们采用了对子组成部分的片状和单维分析模型,以便更好地了解复杂的动态反应;该框架旨在解决在分析和优化对旋转涡轮机包括气动发动机进行故障探测和健康监测的鉴定办法方面所遇到的一些挑战;我们通过将碎块元素和一维有限元素结合起来,模拟碎片和裂缝的振动信号,从而形成一个连接系统;模拟结果与以前公布的数据达成良好协议;我们用有效降低僵硬度的近似法对刀片进行分析,在刀片中模拟裂缝隙;多类故障模型模型,包括单级和两阶段刀片磁盘的刀片、范刀锋off(FBOOO)和外形物体损害(FOD)的裂缝隙。我们运用了节能操作装装饰条件,模拟了在线健康监测的现实情景。拟议中的减序模拟框架将被用于测量振动信号的模型和机器向错误识别。