The condition monitoring of railway track line is one of the essential tasks to ensure the safety of the railway transportation system. Railway track line is mainly composed of tracks, fasteners, sleepers, and so on. Given the requirements for rapid and accurate inspection, innovative and intelligent methods for multi-target defect identification of the railway track line using image processing and deep learning methods are proposed in this paper. Firstly, the track and fastener positioning method based on variance projection and wavelet transform is introduced. After that, a bag-of-visual-word (BOVW) model combined with spatial pyramid decomposition is proposed for railway track line multi-target defect detection with a detection accuracy of 96.26%. Secondly, an improved YOLOv3 model named TLMDDNet (Track Line Multi-target Defect Detection Network), integrating scale reduction and feature concatenation, is proposed to enhance detection accuracy and efficiency. Finally, to reduce model complexity and further improve the detection speed, with the help of dense connection structure, a lightweight design strategy for the TLMDDNet model named DC-TLMDDNet (Dense Connection Based TLMDDNet) is proposed, in which the DenseNet is applied to optimize feature extraction layers in the backbone network of TLMDDNet. The effectiveness of the proposed methods is demonstrated by the experimental results.

Pantograph is one of the most important components in electrical railway vehicles. To guarantee steady power supply for the train, the surface of the pantograph slide plate should be smooth enough so that the catenary can move on it from one side to the other side steadily with low friction. In addition, the thickness of the pantograph slide plate cannot be smaller than the lower limit for the sake of safety. Therefore, periodical inspection and maintenance of the pantograph slide plate are significant in terms of safe and stable operation. In this paper, an innovative and intelligent method based on deep learning and image processing technologies is proposed for the online condition monitoring of the pantograph slide plate. In the first stage, the surface defect detection and recognition method of the pantograph slide plate is proposed. Four typical surface defects of the slide are considered, and a deep learning model, pantograph defect detection neural network (PDDNet), is trained for the defect detection and recognition. In the second stage, five key criteria for qualifying the wear condition are proposed. The wear edge estimation based on image processing technology is investigated in detail. Furthermore, they are used to calculate the wear depth and evaluate the wear condition of the pantograph slide. The experiment results demonstrate that the proposed PDDNet can detect the surface defects and also recognize the four kinds of defects with a sound accuracy. The wear depth estimation results are compared with on-site measurement data, and the proposed method can achieve high estimation accuracy.

Reliability of the railway vehicle suspension system is of critical importance to the safety of the vehicle. On-line health condition monitoring for the suspension system of rail vehicles offers a number of benefits such as preventing further deterioration of vehicle performance, enhancing vehicle safety, increasing operational reliability and availability, and reducing maintenance costs. It is desirable to timely detect the fault and monitor the performance degradation of vehicle suspension systems. In this paper, a comparative study on fault detection methods of urban rail vehicle suspension systems is considered. A novel sensor configuration is proposed where the underlying vehicle system is equipped with only acceleration sensors in the four corners of the carbody, the leading and trailing bogie, respectively. A mathematical model is developed for the considered vehicle suspension system. Both model-based and data-driven approaches are studied for the suspension fault detection problem. The robust observer, the Kalman filter combined with the generalised likelihood ratio test method, the dynamical principle components analysis and the canonical variate analysis approaches are applied to the fault detection problem. The simulation is carried out by means of the professional multi-body simulation tool, SIMPACK. In addition, the advantages and disadvantages of these methods are compared. The simulation results show that the data-driven methods outperform the model-based methods.

The high-speed train has achieved great progress in the last decades. It is one of the most important modes of transportation between cities. With the rapid development of the high-speed train, its safety issue is paid much more attention than ever before. To improve the stability of the vehicle with high speed, extra dampers (i.e. anti-hunting damper) are used in the traditional bogies with passive suspension system. However, the curving performance of the vehicle is undermined due to the extra lateral force generated by the dampers. The active suspension systems proposed in the last decades attempt to solve the vehicle steering issue. However, the active suspension systems need extra actuators driven by electrical power or hydraulic power. There are some implementation and even safety issues which are not easy to be overcome. In this paper, an innovative semi-active controlled lateral suspension system for railway vehicles is proposed. Four magnetorheological fluid dampers are fixed to the primary suspension system of each bogie. They are controlled by online controllers for enhancing the running stability on the straight track line on the one hand and further improving the curving performance by controlling the damper force on the other hand. Two control strategies are proposed in the light of the pure rolling concept. The effectiveness of the proposed strategies is demonstrated by SIMPACK and Matlab co-simulation for a full railway vehicle with two conventional bogies.

Reliability of the railway vehicle suspension system is of critical importance to the safety of the vehicle. It is very desirable to monitor the health condition and the performance degradation of the suspension system online, which offers the important information of the suspension system and is critically important for the condition-based maintenance rather than scheduled maintenance in the future. Advanced fault diagnosis method is one of the most effective means for the health monitoring of the suspension system. In this paper, taking the lateral suspension system as an examcple, the fault isolation issue for different component faults occurring in the suspension system is concerned. The sensor configuration for obtaining the vehicle state information and the mathematical model for the lateral suspension system are presented. Four fault features in the time domain and three fault features in the frequency domain are used for each sensor signal. Three different methods, Dempster–Shafer (D–S) evidence theory, Fisher discrimination analysis (FDA) and support vector machine (SVM) techniques are applied to the fault isolation problem. Simulation study is carried out by means of the professional multi-body simulation tool, SIMPACK. The simulation results show that these methods can isolate the considered component faults effectively with a high accuracy. The D–S evidence-based fault isolation approach outperforms the other two methods.

The railway pantograph carbon contact strip (PCCS) plays a critical role in collecting the electric current from the catenary to guarantee the steady power supply for the train. The catenary contacts with the PCCS and slides from one side to another side when the train runs on the track, which generates the wear on the surface of the PCCS. The thickness of the PCCS cannot be smaller than a lower limit for the sake of safety. Therefore, the remaining useful life (RUL) prediction of the PCCS is beneficial for the pantograph maintenance and inventory management. In this paper, the wear data from Guangzhou Metro are analyzed in the first place. After that, the challenge of predicting the RUL for PCCS from the unequal interval wear data is addressed. A Wiener-process-based wear model and the unequal interval weighted grey linear regression combined model (UIWGLRCM) are proposed for the RUL prediction of the PCCS. The case studies demonstrate the effectiveness of the proposed method via a comparison of RUL prediction with another available method.