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Requirements for current trains to be increasingly available have created the need to develop systems that can predict the quality of both trains and infrastructure components. The paper presents a new approach to the detection of rail truck irregularities, based on the measurements of bearing box acceleration during the operation of rail vehicles. The proposed procedure is based on an inverse problem solution, estimating track irregularities from measured acceleration of the applied model of vehicle dynamics. The simulation study of the proposed method, as well as its implementation, is presented. The method has been successfully applied for the identification of rail irregularities on a typical Polish railroad and vehicle.

The aim of this paper was to develop an equivalent numerical model of a disc valve system used in automotive shock absorbers. Numerical model allows for simulation of fluid flow phenomena through the disc valve system. Paper presents a modeling method and technique of fluid-structure interaction simulations.

This paper considers a method for reconstructing sine-wave excitation using an inverse, parametric data-driven model developed to monitor the magnitude and frequency of the load to which a structure is exposed. The main goal of the paper is to discuss, and experimentally verify, the applicability of different SISO and MIMO structures of parametric models, such as the ARX, ARARX, OE, BJ, and PEM models described by the linear system identification theory. Experimental validation tests were conducted on a set-up consisting of a metal frame equipped with two electrodynamic exciters, several acceleration transducers and a data-acquisition system. The fidelity and adequacy of various model structures was judged in time and frequency domains based on stability diagrams, FPE and AIC criteria, as well as on the magnitude of the relative error between the measured and reconstructed load. The experimental test results showed that, in the case of measurement data moderately corrupted by noise, the ARX and OE models provide better accuracy of inversion than advanced models, such as ARARAX, BJ or PEM. This leads to the conclusion that increasing the complexity of a model structure does not result in better reconstruction of the load. Therefore, less complicated structures are acceptable for practical applications and, in fact, should be favored.