Simulation of Railway Vehicle Dynamics in Universal Mechanism Software

Zheng

2022

Advcd Theory and Sims

The rail fastener is an indispensable component used to connect the rail and sleepers in the track structure. Real-time recognition of the fastener defects plays a vital role in ensuring the safe and stable operation of rail transit. In this paper, an intelligent and innovative method is proposed to detect the fastener defects including the invisible defects appearing as bolt loosening and the visible defects such as the worn or completely missing fasteners by using axle-box vibration acceleration and deep learning network. First, the dynamical relation between the fastener defects and the axle-box vibration acceleration is investigated by using the first principle and the vehicle-track dynamical model. Then a defects recognition network is built based on the deep convolution neural network for track fasteners by using the frequency spectrum images of the axle-box vibration. The results show that the proposed method achieves a classification accuracy of 98.27%. Finally, the track section where the fasteners are most likely to be damaged is investigated, and rail corrugation is found to be a key factor that causes fastener fatigue.

Section: Vibration Axle‐box Vibration Spectrum Due To Fastener Defectsmentioning

confidence: 99%

Railway Track Vibration Analysis and Intelligent Recognition of Fastener Defects

Zheng

2022

Advcd Theory and Sims

“…erefore, the influence of the wheel-rail profile is not considered, while the other twelve parameters obtained from equation ( 9) are further explored in the sensitivity analysis presented in section 2.3. e simulation model used for rail wear calculation is introduced in the next section. With the help of the dynamic software Universal Mechanism (UM) [36], the rail wear produced by vehicles negotiating different curves is calculated for further analysis.…”

Section: Rail Side Wearmentioning

confidence: 99%

Side Wear Prediction of a Subway Outer Rail on Small Radius Curves Based on System Dynamics of Discrete Supported Track

Kou

Discrete Dynamics in Nature and Society

et al. 2022

When a subway train runs on a small radius curve, it causes serious side wear of the outer rail, and a large number of the outer rails of the curve are replaced frequently due to excessive side wear. This paper proposes three prediction models based on the response surface methodology (RSM), support vector machine (SVM), and the relative vector machine (RVM) to estimate the remaining useful life (RUL) of a metro outer rail at a small radius curve determined by rail side wear. In these proposed models, the directly measurable parameters, including various track geometries, axle loads, and primary suspension stiffnesses related to vehicle types, are taken into account. These parameters are proposed from the side wear mechanism and subjected to the method of global sensitivity analysis to rank them in order of importance. The rail side wear for various scenarios is calculated through a numerical simulation model of the vehicle-unballasted track and Specht friction wear. Furthermore, with the help of simulation experiments and field cases, the prediction performances of three different prediction models are analyzed in detail. The comparison indicates that the predictive model based on RVM is superior in the prediction of RUL based on rail wear. The findings presented in this paper can provide certain reference values for infrastructure managers (IMs).

“…e actual support structure under rail is modelled by changing the stiffness and damping of the spring as shown in Figure 4(b). e vehicle-track coupling dynamics model is built by using the dynamic software Universal Mechanism (UM) [64], where the B-type vehicle structure used by Guangzhou Metro is used as the modelling prototype. In this model, the Euler beam is adopted as the rail model, and the total length of the flexible track structure is 500 m. e track irregularity in this model is the measured irregularities of Guangzhou Metro Line 3, where no initial corrugation is found.…”

Section: Simulation Environment and Data Applicationmentioning

confidence: 99%

Applying System Dynamics of Discrete Supported Track to Analyze the Rail Corrugation Causation on Curved Urban Railway Tracks

Discrete Dynamics in Nature and Society

Zheng

2021

Urban rail corrugation on curved tracks with small radii causes strong howling during operation, which has been bothering subway operating companies for many years. Therefore, revealing its causes and growth is important for the comfort and safety of subway operation. Current studies believe that the occurrence of rail corrugation is largely due to the resonant vibration of the wheel-rail system. However, little attention has been paid to the key causes of the track resonance and the practical prediction of the occurrence probability of rail corrugation on the certain track. This paper intends to solve these above issues. Firstly, the practical model of predicting the rail corrugation growth is proposed based on the wheel-rail coupling interaction, the key causes of corrugation are investigated, and the sensitivity analysis is carried out, while the corrugation superposition model is introduced to the analyze the corrugation evolution as well as to validate the corrugation growth from the aspect of material friction and wear. Secondly, the impact of the key causes on the initiation and development of the rail corrugation is investigated based on the cosimulation. Finally, case studies validate the proposed theory model and method. The results show that the practical prediction model for the rail corrugation growth proposed in this paper is able to estimate the occurrence possibility of rail corrugation on a specific track, and the superharmonic resonance of the track directly excited by passing vehicles eventually leads to rail corrugation. It is also found that shortwave corrugation develops more rapidly, and adjusting the support stiffness or sleeper spacing leads to fluctuations in the corrugation wavelength and its wear rate.