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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 angular domain synchronous averaging (ADSA) method for wheel flat detection is proposed. • Simulation and experiment study on the effectiveness of the ADSA method. • This method can remove the interfering signals due to special track geometries or track defects. • This method can identify the position and the number of wheel flats. Wheel flats (WFs) can generate periodic impact forces and aggravate the wheel-rail interaction. Therefore, the early detection of WFs is significant for railway operators to lower the maintenance cost. In this work, the angular domain synchronous averaging (ADSA) method is employed to detect WFs on railway vehicles since it can handle the non-stationary vibration process of rotating machines by processing the vibration data in the angular domain. Furthermore, the short-time irregular impulses, such as those caused by crossings and rail joints, can be attenuated by using synchronous averaging technology. The periodic impact caused by WFs can be easily observed in the polar coordinate using the ADSA method, which is verified by one multibody system (MBS) simulation and one field experiment. Besides, the sensitivity of this method to various flat dimensions and multiple flats is also studied in the MBS simulation. [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]