Experimental unbalance identification by means of correlation analysis and model order reduction

Abstract

The unbalance is one of the most common faults in rotor systems and it can cause severe damage during the rotor operation. There are many unbalance detection methods that have been proposed up to now by many researchers around the world, justifying the importance of this theme. This paper proposes a model based identification method to identify the unbalance parameters magnitude and location that avoids the usage of the complete set of rotor responses. This is an initial study that combines the Guyan reduction and the correlation analysis to generate an identification algorithm in time domain, which comes from the Lyapunov matrix equation, able to identify the unbalance parameters. It was used a Laval rotor supported by two rolling bearings whose physical characteristics, stiffness and damping, were determined by optimization using the Differential Evolution method to compare the experimental FRF with the optimized simulated one.