A reduced-order modeling framework for simulating signatures of faults in a bladed disk
This work addresses challenges in fault detection and identification for rotating turbomachinery, such as aero-engines, by providing a simulation tool for health monitoring, though it appears incremental as it builds on existing modeling approaches.
The paper developed a reduced-order modeling framework to simulate vibration signatures of faults like cracks in bladed disks on rotating shafts, aiming to support data-driven machine learning for health monitoring, with simulation results showing good agreement with published data.
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.