Herman Van der Auweraer

TL;DR: In this paper, the effect of the cross-coupling between the input signals was examined and compared to a reliable classical TPA measurement, and the results revealed three significant weaknesses.

TL;DR: In this paper, an online estimation of the inertial parameters of a road-vehicle is presented, based on low-frequent suspension displacement signals and suspension stiffness characteristics, using a Monte Carlo method to determine the most probable mass distribution.

Abstract: In the classical modal parameter estimation approach, the baseline data which are processed are Frequency Response Functions measured in laboratory conditions However, in many applications, the real operating conditions may differ significantly from those applied during the modal test (suspension pre-strains of a car on the road, large motion of a running engine, aeroelastic interaction and wing/rotor pre-strain in air- and rotorcraft, strong environmental excitation in civil structures as traffic load, wind/wave excitation, ) Hence, the need arises to identify a modal model in these real operational conditions In most cases, only response data are measurable while the actual loading conditions are unknown A number of different estimation techniques have been evaluated for their applicability in such conditions They include the Polyreference Least Squares Complex Exponential method and an Auto-Regressive Vector model method The basic principles of these methods are reviewed and applied to an experimental case

TL;DR: In this article, an approach for automating the modal parameter estimation process and its industrial validation is presented, which is based on the PolyMAX modal estimation method, which makes the estimation process much easier by better discriminating spurious from physical poles, in particular in the case of high-order and highly damped structures.