The train track ground interaction vibration due to a speedy moving load on the track and the induced ground vibration transmission alongside are analyzed. The track is modeled with a Winkler bending beam on the ground that is modeled with stratum on rigid base. The dynamic response of the ground base under a moving load is obtained. The findings make the useful basis for predicting the case of train induced vibration.
The whole body vibration of a recumbent passenger in a railway sleeper carriage is the major effect on the comfort feeling.The vertical vibration model of a supine human body was studied.Based on the secondary suspension dynamic model of railway vehicles,a 14 DOFs human-berth-vehicle spatial vertical coupled dynamic model was established considering the vibration isolation effect of the sleeping berth and the vertical vibration characteristics of the supine human body.Under the random irregular excitation of track on the coupled dynamic model,the vertical vibration responses of the supine human body were studied at different train speeds.With the evaluation criterion of the vibration comfort of human body in supine position,the simulation program for the vibration comfort of human body in a railway sleeper carriage was established.The root-mean-square accelerations of head and buttock were weighted using body positions and frequencies in succession to get the vibration comfort index of the human body in a railway sleeper carriage.Simulation software for the vibration comfort of the human body in a railway sleeper carriage was developed.After inputting the structural and dynamic parameters of the human body and the vehicle,the simulating process for the vibration comfort of the human body could be carried out automatically.Therefore it provided an effective means to analyze the vibration comfort of the human body in a railway sleeper carriage and optimize the suspension parameters of the vehicle.
To enhance the safe operation of modern railway vehicles, an online condition monitoring scheme is proposed for vehicle suspension systems. The core technology of the scheme is based on the average correlation signals based stochastic subspace identification (ACS-SSI) algorithm which allows system identification to be implemented reliably with output signals only that have strong noise and nonlinearity in vehicle applications. To validate the scheme, a series simulation studies were carried out based on a more realistic bogie model, developed in SIMPACK, under typical random excitations including vertical, lateral, rolling and gauging directions. ACS-SSI is then applied to the signals from the model under common faults in the bogie suspensions to identify the system parameters. The agreeable results obtained by comparing the identified results with that calculated by SIMPACK shows that the proposed scheme performs reliably in obtaining the system parameters: modal frequency, damping and shape that are required for online diagnosis.
State estimation of nonlinear dynamic systems is an important problem in practice. This paper proposes a recursive state estimation method for nonlinear dynamic systems using Gaussian processes (GP) and pre-computed local linear models. Gaussian processes exhibit remarkable learning, or nonlinear regression capabilities from measurement data. The incorporation of pre-computed local linear models reduces the amount of data required and improves the regression performance of the GP. Based on such an improved GP model for nonlinear dynamic systems, a recursive Bayesian filtering method is implemented for the estimation of the unknown states of the system. Simulations on an aircraft benchmark model demonstrate that the proposed method is capable of estimating the unknown angle of attack of the aircraft, and the existence of the local linear models significantly improves the estimation performance of the filter. This method is especially useful in a control systems design context in which local linearisations of nonlinear dynamic systems are usually readily available.
This paper reports fundamental research on the general conditions for railway wheel polygonal wear to evolve. A common workflow for prediction of railway wheel polygonization is presented including assumptions, simulation scheme, and wear models. Based on this workflow, some rules for the evolution of railway wheel polygonization are proposed providing innovative perspectives to understand the basic mechanism of railway wheel polygonization. After summarising these rules, the general conditions for railway wheel polygonal wear to evolve are established. The phase between the instantaneous wear depth and the excitation is the key indicator determining the wheel OOR (Out-Of-Roundness) evolution direction (to grow or to diminish). The evolution tendency curve obtained from the instantaneous wear FRF (Frequency Response Function) is a useful tool to predict the OOR evolution, especially for predicting the OOR order that would grow predominantly at a given speed. If one or more structural modes can dominate the evolution tendency curve, and the energy distribution of track excitation allows this/these structural modes to be excited effectively, corresponding OOR orders can occur dominantly.
The friction coefficient at the wheel–rail interface is crucial for the traction, braking and guidance of the railway vehicle. Real-time knowledge of the friction coefficient could reduce wheel–rail damages as well as improve the vehicle running performance. The friction coefficient is very difficult to be directly measured; hence an indirect measurement method using an unscented Kalman filter was proposed in this paper. A re-adhesion controller was also developed. The method was assessed in a Simpack-Simulink co-simulation environment. The friction coefficient estimation method was found to be accurate, and the re-adhesion controller improved the vehicle braking performance as well as reduced the wheel–rail damage.
The trend item of a long-term vibration signal is difficult to remove. This paper proposes a piecewise integration method to remove trend items. Examples of direct integration without trend item removal, global integration after piecewise polynomial fitting with trend item removal, and direct integration after piecewise polynomial fitting with trend item removal were simulated. The results showed that direct integration of the fitted piecewise polynomial provided greater acceleration and displacement precision than the other two integration methods. A vibration test was then performed on a special equipment cab. The results indicated that direct integration by piecewise polynomial fitting with trend item removal was highly consistent with the measured signal data. However, the direct integration method without trend item removal resulted in signal distortion. The proposed method can help with frequency domain analysis of vibration signals and modal parameter identification for such equipment.
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