Elimination of Bias Errors Due to Suspension Effects in FRF-Based Rigid Body Property Identification

Abstract

The prediction of a mechanical structure’s rigid dynamic behavior requires knowledge of ten inertia parameters. In cases where no accurate models of the structure’s geometry and mass distribution are available, the ten inertia parameters must be determined experimentally. Experimental methods based on measurements of frequency response functions (FRFs) are subject to bias errors due to suspension effects. This paper proposes a method for eliminating these errors by using a single-wire suspension condition and modeling the suspension’s effect on the FRFs. The suspension model depends only on the unknown rigid body properties and on three easy-to-measure parameters. The rigid body properties are determined by fitting FRFs derived from the suspension model and from the rigid body mass matrix directly to the experimental FRF data. Eliminating the suspension bias makes it possible to use low-frequency FRF data, which in turn justifies the assumption of rigid behavior. In this way, bias-free rigid body property identification can be achieved without modal curve fitting. Simulation and experimental results are presented showing the effectiveness of the approach.