System and source identification from operational vehicle responses: A novel modal model accounting for the track-vehicle interaction.

Operational Modal Analysis (OMA) is a powerful tool, widely used in the fields of structural identification and health monitoring, and certainly eligible for identifying the real in-operation behaviour of vehicle systems. Several attempts can be found in the literature, for which the usage of algorithms based on the classical OMA formulation has been strained for the identification of passenger cars and industrial trucks. The interest is mainly focused on the assessment of suspension behaviour and, thus, on the identification of the so-called vehicle rigid body modes. But issues arise when the operational identification of a vehicle system is performed, basically related to the nature of the loads induced by the roughness of rolling profiles. The forces exerted on the wheels, in fact, depending on their location, are affected by time and/or spatial correlation, and, more over, do not fit the form of white noise sequences. Thus, the nature of the excitation strongly violate the hypotheses on which the formulation of classical OMA modal model relies, leading to pronounced modelling errors and, in turn, to poorly estimated modal parameters. In this paper, we develop a specialised modal model, that we refer to as the Track-Vehicle Interaction Modal Model, able to incorporate the character of road/rail inputs acting on vehicles during operation. Since in this novel modal model the relationship between vehicle system outputs and modal parameters is given explicitly, the development of new specific curve fitting techniques, in the time-lag or frequency domain, is now possible, making available simple and cost-effective tools for vehicle operational identification. More over, a second, but not less important outcome of the proposed modal model is the usage of the resulting techniques for the indirect characterisation of rolling surface roughness, that can be used to improve comfort and safety.