چكيده به لاتين
Concurrent measurement of the wheel-rail contact force is always a crucial factor in design of safe railway vehicles. Commonly, instrumented wheelset has been developed towars this goal. In the first place of this study, a novel technique for precise design of the instrumented wheelset, which estimates static forces, is presented. Influences of different factors such as the wheel rotation, the temperature or the centrifugal force are eliminated by cancelation of the higher harmonics. The accuracy of estimated forces is dependent on the placement of gauges on the instrumented wheelset. A solution procedure in terms of mean square error is implemented to determine the optimal radial locations of strain gauge that will provide the most precise force measurement. Validation of the proposed method through numerical simulation is also presented. We show that the effectiveness and robustness of the technique are preserved even in the presence of noise in strain signal. In addition to the linear model for force estimate, artificial neural network (ANN) is adopted to estimate forces more robustly. Multilayer perceptron in conjunction with the Bayesian training framework is applied in this study. At the end, accuracy and validation of the two approaches in estimation of the static forces are compared by numerical study for noise-free and noise-contaminated cases.
In the second place, dynamic wheel-rail contact forces are estimated using a novel indirect identification method based on the measured radial strain on the wheel web. Strain response of the rolling wheel is derived using the analytical solution for the disk under rotating load here to sketch out a scheme for identification of the rolling wheel parameters and its corresponding characteristic matrix. An appropriate angular strain configuration is employed to eliminate the effect of the wheel rotation. Tikhonov regularization technique is employed to solve the ill-pose least square problem and to attenuate the effect of noisy measurement and numerical uncertainty in estimation of the forces. Finite element model of the rotating load is then constructed to investigate the effectiveness and accuracy of the proposed methodology. Effects of the rotating speed and loading and measurement noise on the estimated normal force are studied. It is found that neglecting the effect of rotating speed causes a remarkable error particularly in high-speed range.
Key Words: Instrumented wheelset, Sensor placement, Contact force estimation, Structural identification, Tikhonov regularization, Finite element model
