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Small-radius wheels are gradually introduced on special rail vehicles, such as double-decker coaches and piggyback wagons. It allows for a low-floor design and further increases the loading capacity of vehicles. From the perspective of vehicle dynamics, small-radius wheels have lower unsprung mass than the nominal wheels with a radius of 460 mm, and it could mitigate the wheel-rail dynamic interaction. Also, the contact patch size is reduced due to the increased curvature of the rolling contact circle, and they are more sensitive to changes in the track geometry than nominal wheels, especially in turnouts with discontinuous rail profiles. To investigate the dynamic response of small-radius wheels in a turnout, the Manchester Benchmark passenger coach with variable radius wheels and a left-hand turnout with a radius of 245 m are introduced in this work. The influence of wheel radii on the running performance, including the wheel-rail interaction and wheel damage behaviour, is investigated by numerical simulations. In addition, a sensitivity study is executed to evaluate the influence of unsprung mass and wheel flange friction on the performance of a vehicle whose wheel radius is 275 mm, and the potential remedy to mitigate high wheel wear is proposed. [ABSTRACT FROM AUTHOR]

Recently, composite brake blocks have been widely adopted in European railway systems to replace cast iron brake blocks in order to reduce braking noise. However, the poor heat dissipation of composite materials has caused more wheel tread damage, including wheel flats (WFs). The WF, as an undesirable tread defect in railway vehicles, can cause severe wheel/rail impact and increase the damage probability of wheelset components and track structures. Early detection of WFs is vital to ensure operational safety and reduced maintenance costs. In this work, to help understand the dynamic impact caused by WFs, a single WF with a length of 20 mm is artifically produced on a wheel of a tank wagon, and field experiments are performed. The influence of the WF on the axle box accelerations (ABAs), vertical accelerations of the bogie frame and vertical accelerations of the car body is discussed in the time and time-frequency domains. On the other hand, the angular domain synchronous averaging (ADSA) method is used to detect WFs by averaging the ABA signals in the angular domain over a fixed time interval, and it requires ABA data and the axle rotational speed as inputs. This method can greatly reduce the amount of measured data by averaging it in the angle domain and can eliminate the short-time influence of interference signals caused by switches and crossings, rail joints, and so on. The results show that the ADSA of a defective wheel with the WF has a clear peak with an amplitude of 21–104 m/s2 while that of a healthy wheel fluctuates around zero in the polar coordinate. Also, the waveform of defective signals can be well presented in polar coordinates. The effectiveness of this method is compared with the envelope spectrum technology in three scenarios and its application to three journeys is presented. [ABSTRACT FROM AUTHOR]

In addition to the discontinuity of the rail profile of a normal turnout, the unique S-shaped curve of a crossover significantly affects the negotiation performance of a train. For heavy-haul freight trains, the in-train forces generated during the braking process may further deteriorate the running safety. Investigating the dynamic behaviour of the train passing through the crossover under braking forces, therefore, can provide a reasonable basis for ensuring train safety. This paper analyses the dynamic response and wheel wear of a three-vehicle train negotiating a single crossover under variable conditions, including variable braking forces, wheel-rail friction coefficient, and crossover layout forms. The results show that the three factors have considerable influences on train safety. If one of the three factors keeps an ideal level, the safe probability of empty wagons can be significantly improved. Considering that there are still some crossovers with tight curves in the existing railway system, avoiding large braking or lubricating the wheel flange in the crossover is still an effective solution to improve the vehicle's performance. In addition, for a new single crossover, a 600-m-radius curve is a good option for balancing permissible train speeds and construction costs of the crossover. [ABSTRACT FROM AUTHOR]

The existing multi-objective wheel profile optimization methods mainly consist of three sub-modules: (1) wheel profile generation, (2) multi-body dynamics simulation, and (3) an optimization algorithm. For the first module, a comparably conservative rotary-scaling fine-tuning (RSFT) method, which introduces two design variables and an empirical formula, is proposed to fine-tune the traditional wheel profiles for improving their engineering applicability. For the second module, for the TRAXX locomotives serving on the Blankenburg–Rübeland line, an optimization function representing the relationship between the wheel profile and the wheel–rail wear number is established based on Kriging surrogate model (KSM). For the third module, a method combining the regression capability of KSM with the iterative computing power of particle swarm optimization (PSO) is proposed to quickly and reliably implement the task of optimizing wheel profiles. Finally, with the RSFT–KSM–PSO method, we propose two wear-resistant wheel profiles for the TRAXX locomotives serving on the Blankenburg–Rübeland line, namely S1002-S and S1002-M. The S1002-S profile minimizes the total wear number by 30%, while the S1002-M profile makes the wear distribution more uniform through a proper sacrifice of the tread wear number, and the total wear number is reduced by 21%. The quasi-static and hunting stability tests further demonstrate that the profile designed by the RSFT–KSM–PSO method is promising for practical engineering applications. [ABSTRACT FROM AUTHOR]

Failures of rotating mechanical components (e.g., turbine, gear, wheelset) often cause serious shocks to the mechanical system, and real-time detection of these shocks is of importance in maintenance decision-making for the equipment. The service conditions (e.g., rotating speed, load) of rotating machinery are often complex, and therefore the self-adaptability and generalizability of shock detection methods under variable operating conditions is an issue worthy of in-depth study. In this paper, a novel hybrid method combining a threshold-based method for feature extraction and a machine-learning-based method for pattern recognition is developed. This method consists of two steps. First, an adaptive feature called activated time-domain image (ATDI) is proposed, where two adaptive activation functions are proposed to activate the time-domain vibration signals after being preprocessed. The resulting ATDI feature image is highly adaptive and changes adaptively depending on the operating conditions. Then, a hybrid method combining ATDI and deep neural network (ATDI-DNN) is developed, where a circshift-based data augmentation method is introduced for enriching the ATDI feature images. Finally, the proposed ATDI-DNN method is used for wheel flat detection of a railway vehicle under variable operating conditions. Experiments demonstrate that the ATDI-DNN model trained with samples from one speed level can be directly applied to other speed levels, and its superiority is demonstrated by comparative methods. The proposed method can be extended to shock detection of other similar rotating machinery. [ABSTRACT FROM AUTHOR]