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The purpose of this study is to match appropriate friction coefficients for subway operational vehicles, considering the dynamic variations of wheel profile wear.

This study combines experimental testing and numerical simulation to investigate the influence of wheel profile wear coupled with the friction coefficient on the vehicle dynamic response.

For the test route in this paper, it is recommended to control the friction coefficient on straight sections between 0.25 and 0.3, and on curved sections between 0.2 and 0.3. This satisfies the required adhesion coefficient for normal train traction and braking, while also ensuring the straight running performance and curve negotiation performance of the vehicle.

Reasonable friction coefficient ranges are proposed for straight and curved track lines to improve the operational safety and economy of the vehicles. Moreover, this study can provide a theoretical basis and reference direction for developing anti-wear measures for rail vehicles operating on fixed routes.

Considering the wear characteristics of operating vehicles and the dynamic changes in the wear profile, this paper explores the adaptability of different degrees of wheel wear profiles to different friction coefficients. Based on the response characteristics of vehicle dynamics, reasonable lubrication recommendations are proposed for this operating vehicle.

Considering that a meet between high-speed trains can generate aerodynamic loads, this study aims to investigate the effect of high-speed train meet on wheel wear at different speeds to provide a more accurate wheel wear model and a new idea for reducing wheel wear.

The train speed was set at 250, 300, 350 and 400 km/h separately, and a vehicle system dynamics model was constructed using the parameters of an actual high-speed train on a line. The aerodynamic forces arising from constant-speed train meet were then applied as additional excitation. Semi-Hertzian theory and Kalker’s simplified theory were used to solve the wheel/rail contact problems. The wheel wear was calculated using Archard wear model. The effect of train meet on wheel wear was analyzed for the whole train, different cars and different axles.

According to the results, all wheels show a wear increase in the case of one train meet, compared to the case of no train meet. At 250, 300, 350 and 400 km/h, the total wheel wear increases by 4.45%, 4.91%, 7.57% and 5.71%, respectively, over the entire operational period. The change in speed has a greater impact on wheel wear increase in the head and tail cars than in the middle car. Moreover, the average wear increase in front-axle wheels is 1.04–2.09 times that in rear-axle wheels on the same bogie.

The results will help to analyze wheel wear more accurately and provide theoretical guidance for wheel repair and maintenance from the perspective of high-speed train meet.

At present, there is a lot of focus on the impact of high-speed train meet on the dynamic performance of vehicles. However, little research is available on the influence of train meet on wheel wear. In this study, a vehicle dynamics model was constructed and the aerodynamic forces generated during high-speed train meet were applied as additional excitation. The effect of train meet on wheel wear was analyzed for the whole train, different cars and different axles. The proposed method can provide a more accurate basis for wear prediction and wheel repair.

This study aims to investigate the effect of time-varying passenger flow on the wheel wear of metro vehicles to provide a more accurate model for predicting wheel wear and a new idea for reducing wheel wear.

Sectional passage flow data were collected from an operational metro line. A wheel wear simulation based on time-varying passenger flow was performed via the SIMPACK software to obtain the worn wheel profile and wear distribution. The simulation involves the following models: vehicle system dynamics model, wheel-track rolling contact model, wheel wear model and variable load application model. Later, the simulation results were compared with those obtained under the traditional constant load condition and the measured wear data.

For different distances traveled by the metro vehicle, the simulated wheel profile and wear distribution under the variable load remained closer to the measurements than those obtained under the constant load. As the distance traveled increased, the depth and position of maximum wear and wear growth rate under the variable load tended to approach the corresponding measured values. In contrast, the simulation results under the constant load differed greatly from the measured values. This suggests that the model accuracy under the variable load was significantly improved and the simulation results can offer a more accurate basis for wear prediction.

These results will help to predict wheel wear more accurately and provide a new idea for simulating wheel wear of metro vehicles. At the same time, measures for reducing wheel wear were discussed from the perspective of passenger flow changes.

Existing research on the wheel wear of metro vehicles is mainly based on the constant load condition, which is quite different from the variable load condition where the passenger flow in real vehicles varies over time. A method of simulating wheel wear based on time-varying load is proposed in this paper. The proposed method shows a great improvement in simulation accuracy compared to traditional methods and can provide a more accurate basis for wear prediction and wheel repair.

The purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for addressing the non-linearity of the load time series.

Short-term load forecasting is a complex process as the nature of the load-time series data is highly nonlinear. So, only ARIMA-based load forecasting will not provide accurate results. Hence, ARIMA is combined with MLP, a deep learning approach that models the resultant data from ARIMA and processes them further for Modelling the non-linearity.

The proposed hybrid approach detects the residuals of the ARIMA, a linear statistical technique and models these residuals with MLP neural network. As the non-linearity of the load time series is approximated in this error modeling process, the proposed approach produces accurate forecasting results of the hourly loads.

The effectiveness of the proposed approach is tested in the laboratory with the real load data of a metropolitan city from South India. The performance of the proposed hybrid approach is compared with the conventional methods based on the metrics such as mean absolute percentage error and root mean square error. The comparative results show that the proposed prediction strategy outperforms the other hybrid methods in terms of accuracy.