  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.
