A reduced-order modeling framework for simulating signatures of faults in a bladed disk

This paper reports a reduced-order modeling framework of bladed disks on a rotating shaft to simulate the vibration signature of faults in different components aiming toward 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 addresses some of the challenges encountered in analyzing and optimizing fault detection and identification schemes for health monitoring of aero-engines and other rotating machinery. 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 and analyze the cracks in a blade with their effective reduced stiffness approximation. Different types of faults are modeled, including cracks in the blades of a single and a two-stage bladed disks, Fan Blade Off (FBO), and Foreign Object Damage (FOD). We have applied aero-engine operational load 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. Keywords: Reduced-order model, health monitoring, rotating system, engine, fault, crack, FBO, FOD 1. Introduction For decades, health monitoring system to detect faults in rotors has remained a significant area of interest, particularly to the aero-engine industry and turbine-based power plant operators. Structural Health Monitoring (SHM) system design typically deals with performance-related challenges involving sensors, interfaces, and software algorithms to efficiently function under harsh environmental conditions and detect faults from noisy and complex signals. Verification and validation of the overall system performance through modeling signals have great promises.