Rolling bearings are the most prone components to failure in urban rail trains, presenting potential danger to cities and their residents. This paper puts forward a rolling bearing fault diagnosis method by integrating empirical mode decomposition (EMD) and genetic neural network adaptive boosting (GNN-AdaBoost). EMD is an excellent tool for feature extraction and during which some intrinsic mode functions (IMFs) are obtained. GNN-AdaBoost fault identification algorithm, which uses genetic neural network (GNN) as sub-classifier of the boosting algorithm, is proposed in order to address the shortcomings in classification when only using a GNN. To demonstrate the excellent performance of the approach, experiments are performed to simulate different operating conditions of the rolling bearing, including high speed, low speed, heavy load and light load. For de-nosing signal, by EMD decomposition is applied to obtain IMFs, which is used for extracting the IMF energy feature parameters. The combination of IMF energy feature parameters and some time-domain feature parameters are selected as the input vectors of the classifiers. Finally, GNN-AdaBoost and GNN are applied to experimental examples and the identification results are compared. The results show that GNN-AdaBoost offers significant improvement in rolling bearing fault diagnosis for urban rail trains when compared to GNN alone.

Rail surface defects typically serve as early indicators of railway malfunctions, which may compromise the quality and corrosion resistance of rails, thereby endangering the safe operation of trains. The timely detection of defects is essential to ensure the safe operation of railways. To improve the classification accuracy of rail surface defect detection, this paper proposes a rail surface defects detection algorithm based on MobileNet-YOLOv7. By integrating lightweight deep learning algorithms into the engineering application of rail surface defect detection, a MobileNetV3 lightweight network is used as the backbone network for YOLOv7 to enhance both speed and accuracy in complex defect extraction. Subsequently, the efficient intersection over union (EIOU) loss function is utilized as the positional loss function to bolster system resilience. Finally, the k-means++ clustering algorithm is applied to obtain new anchor boxes. The experimental results demonstrate the effectiveness of the proposed method, achieving superior detection accuracy compared with traditional algorithms.

The RAMS (reliability, availability, maintainability, and security) of the air braking system is an important indicator to measure the safety performance of the system; it can reduce the life cycle cost (LCC) of the rail transit system. Existing safety analysis methods are limited to the level of relatively simple factual descriptions and statistical induction, failing to provide a comprehensive safety evaluation on the basis of system structure and accumulated data. In this paper, a new method of safety analysis is described for the failure mode of the air braking system, GO‐Bayes. This method combines the structural modeling of the GO method with the probabilistic reasoning of Bayes methods, introduces the probability into the analysis process of GO, performs reliability analysis of the air braking system, and builds a big data platform for the air braking system to guide the system maintenance strategy. An automatic train air braking system is taken as an example to verify the usefulness and accuracy of the proposed method. Using ExtendSim software shows the feasibility of the method and its advantages in comparison with fault tree analysis.

Tunnel positioning of vehicles usually has large errors and disturbances. In this paper, the tunnel electromagnetic wave propagation model is analyzed. Based on this, an RSSI positioning algorithm based on LMLF(Linear Modified Log Function) model is proposed. The constraint factor in this algorithm can reduce the positioning error of the fitting curve, and Gaussian-median filtering technology enhances the Robustness of ranging. Experiments show that the RSSI positioning algorithm based on LMLF greatly improves the accuracy and anti-interference ability of RSSI positioning.

Communication-based train control systems (CBTCs) have been widely used as crucial systems in urban rail transit networks. CBTCs typically utizes different levels of symmetry structure according to different geographic deployments. While, in practice, CBTCs crashes have destroyed the transportation systems of the whole city level for many times. Based on the extended object-oriented Petri net (EOOPN), this paper proposes a vulnerability model and an evaluation procedure, which are capable of considering the vulnerability factors in both inner system level and equipment level. On the system level, it establishes a complex dynamic communication structure model among the distributed subsystems, while on the equipment level, it details the equipment changing state during train operation. The searching algorithm of EOOPN depicts possible failed paths of CBTCs via the token transition among train¬–ground communication EOOPN subnets. The vulnerability calculation is applied to the metro company’s in situ CBTCs to illustrate the effectiveness of the approach.