The effective prediction of the remaining useful lifetime (RUL) of the railway track can provide key information for intelligent operation and maintenance. However, accurate prediction of railway track RUL is still challenging due to the issues such as serious data noises, large sampling data interval, and small samples in the track inspection dataset, which are usually caused by the complex and various operating environment. To address these issues, a new prediction method for railway track RUL, which is the combination of Dynamic Multi-Scale Gated Dilated Causal Convolution (DMGDCC)and Gated Recurrent Unit (GRU) called DMGDCC-GRU, is proposed in this work. Firstly, an adaptive filtering module was constructed based on dynamic convolution to solve the problem of insufficient feature extraction that is caused by time-varying noise interference in the data. Secondly, a feature extraction network with multi-scale gated dilated causal convolution structure was designed to monitor the damage feature at different scales as well as to generate the boundary representation for the degree of damage at each time-step by the gating mechanism. Based on these, a GRU, which can improve the performance of time-series feature extraction, was introduced to learn the feature of damage trend in full life cycle of railway track efficiently. Finally, an innovative pre-training method for transfer learning from bearing to rail was developed to mitigate the problems about the small samples and long sampling interval. Among the many state-of-the-art RUL prediction methods, experimental evaluations with the actual rail degradation datasets showed that the proposed method can achieve 38.9% reduction in RMSE and 13.4% increase in R ² (Degradation due to rail peeling), 31.7% reduction in RMSE and 8.2% increase in R2(Degradation due to rail corrugation). Significantly, the validation results of rolling bearing datasets also proved the high generalization performance of the proposed method.

The traction performance of heavy-haul locomotive is subject to the wheel/rail adhesion states. However, it is difficult to obtain these states due to complex adhesion mechanism and changeable operation environment. According to the influence of wheel/rail adhesion utilization on locomotive control action, the wheel/rail adhesion states are divided into four types, namely normal adhesion, fault indication, minor fault, and serious fault in this work. A wheel/rail adhesion state identification method based on particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is proposed. To this end, a wheel/rail state identification model is constructed using KELM, and then the regularization coefficient and kernel parameter of KELM are optimized by using PSO to improve its accuracy. Finally, based on the actual data, the proposed method is compared with PSO support vector machines (PSO-SVM) and basic KELM, respectively, and the results are given to verify the effectiveness and feasibility of the proposed method.

This paper investigates a virtual line shafting-based total-amount coordinated control method of multi-motor traction power to solve the traffic safety problem caused by train traction power loss. This method considers the total amount instead of the synchronous control amongst single motors in a multi-motor control system. Firstly, a block diagram of the proposed method is built. Secondly, on the basis of this diagram, an accurate system model with parameter perturbations is constructed. Thirdly, a virtual controller is designed to quickly adjust the output torque of the virtual motor and to realise a tracking control of the reference torque. A total-amount coordinated control strategy based on the integral sliding mode is also designed to keep the total traction power of the multi-motor system constant under uncertain and unknown disturbances. Lyapunov stability theory is used to prove the system stability. The simulation and experiment results verify the effectiveness of the virtual controller and the total-amount coordinated control strategy in guaranteeing system robustness under disturbances and parameter perturbations.

Traffic flow prediction is an important part of an intelligent transportation system to alleviate congestion. In practice, most small and medium-sized activities are not given priority in transport planning, yet these activities often bring about a surge in demand for public transport. It is recognized that such patterns are inevitably more difficult to predict than those associated with day-to-day mobility, and that forecasting models built using traffic data alone are not comprehensive enough. Aiming at this problem, a depthwise separable convolutional fusion forecast network (FFN) was proposed by focusing on the impact of event information on traffic flow demand. FFN fused heterogeneous data to model traffic data, weather information, and event information extracted from the Internet. The depthwise separable one-dimensional convolution was used to encode the textual information describing the event layer by layer, and local one-dimensional sequence segments (ie subsequences) were extracted from the sequence to retain rich local semantic features. In the modeling process, the interaction of heterogeneous data was established, that is, the temporal and other data were used to drive the textual information representation in the encoding process to capture better relevant textual representations. Finally, information from different sources and formats was fused to obtain a joint feature representation tensor that predicts the traffic demand in the next day's event area. The experimental results show that the average absolute error of the fusion prediction network is reduced by 26.5%, the root mean square error is reduced by 11.6%, and the judgment coefficient is increased by 26.4% compared with the prediction network that only considers the traffic data.

Due to the long-term service through wheel-rail rolling contact, the train wheelset tread will inevitably suffer from different types of defects, such as wear, cracks, and scratches. The effective detection of wheelset tread defects can provide critical support for the operation and maintenance of trains. In this paper, a new method based on a local inference constraint network is proposed to detect wheelset tread defects, and the main purpose is to address the issue of insufficient feature spaces caused by small samples. First, a generative adversarial network is applied to generate diverse samples with semantic consistency. An attention mechanism module is introduced into the feature extraction network to increase the importance of defect features. Then, the residual spine network for local input decisions is constructed to establish an association between sample features and defect types. Furthermore, the network’s activation function is improved to obtain higher learning speed and accuracy with fewer parameters. Finally, the validity and feasibility of the proposed method are verified using experimental data.

Prolonged rolling contact fatigue between wheels and rails results in the formation of surface cracks on the rail and accurately analyzing the crack expansion behavior is essential to ensuring the safe operation of the train. Drawing upon the principles of fracture mechanics and finite element theory, this study establishes a finite element model of wheel–rail rolling contact that incorporates the presence of cracks. The method utilizes an interaction integral to calculate the stress intensity factors at the leading edge of the crack; then, the Paris formula is used to solve the crack spreading rate. It systematically examines the effects of the initial crack angle, the coefficient of friction of wheels to rails, and crack size on the behavior of fatigue crack propagation. The results indicate that the cracks primarily extend in the depth direction of the rail, transforming the semi-circular surface cracks into elliptical cracks with the major axis oriented along the rail’s width. Crack propagation is primarily driven by model II and III composite crack propagation, with their expansion rates influenced by operating conditions. In contrast, mode-I expansion is less sensitive to these conditions. Under single-variable loading conditions, a smaller initial crack angle results in a faster crack growth rate. Increasing crack length accelerates crack growth, while a higher friction coefficient inhibits it.