Weidong Li

Train Control Onboard Systems are typical systems capable of operating in degraded modes. Under normal conditions, they maintain operation at the highest functional level, while under specific exceptional circumstances, they can downgrade to a lower operational level to sustain service, thereby preventing train stoppages due to partial system failures. However, existing research on the resilience of Train Control Onboard Systems predominantly focuses on the impact of system performance degradation on resilience, often overlooking the influence of system degraded operation on both system performance and overall resilience. To address this gap, this paper proposes, for the first time, an Influence Index of EMU Operation Distance Deviation to quantify the impact of system degradation caused by sudden failures on train operation. The train operation under such scenarios is divided into three stages: degradation deceleration, degraded operation, and upgrade acceleration. Using availability as the resilience metric, a quantitative resilience model for the system under this scenario is established. Finally, taking a ground base station failure in the Chinese high-speed railway as a case study, a resilience assessment of the train control onboard system is conducted. The results indicate that degradation to CTCS-2 operation mitigates the expanding trend of the impact of the ground base station failure on train operation. Furthermore, it was found that the manifestation of the onboard system’s resilience exhibits hysteresis.

Bearings are key components of modern mechanical equipment. To address the issue that the limited information contained in the single-source signal of the bearing leads to the limited accuracy of the single-source fault diagnosis method, a multi-sensor fusion fault diagnosis method is proposed to improve the reliability of bearing fault diagnosis. Firstly, the feature extraction process of the convolutional neural network (CNN) is improved based on the theory of variational Bayesian inference, which forms the variational Bayesian inference convolutional neural network (VBICNN). VBICNN is used to obtain preliminary diagnosis results of single-channel signals. Secondly, considering the redundancy of information contained in multi-channel signals, a voting strategy is used to fuse the preliminary diagnosis results of the single-channel model to obtain the final results. Finally, the proposed method is evaluated by an experimental dataset of the axlebox bearing of a high-speed train. The results show that the average diagnosis accuracy of the proposed method can reach more than 99% and has favorable stability.

In order to improve the detection accuracy of urban tram track obstacles running in complex environments, a track obstacle detection algorithm based on BF-SSD is proposed. Design convolutional splitting structure, replace all 3*3 convolutional layers in VGG16 except the first convolutional layer, improve the detection speed, design two-way fusion modules, strengthen the feature expression ability of the low-level feature layer, improve the detection accuracy of small targets, design a two-stage deconvolution module, make up for the noise information generated by the high-level feature layer due to excessive deconvolution, enhance the detection effect in harsh environments, propose to improve NMS, and improve the detection ability of coincident targets. Through the test and analysis of the obstacle image dataset of the self-made normal environment and the harsh environment, the experimental results show that compared with other algorithms, the loss function of the BF-SSD algorithm is the lowest, and the mAP of the BF-SSD algorithm reaches 90.03% and 87.83% respectively under normal weather and bad weather environments, and the mAP of the detection of small targets reaches 70.26% and 69.26%, respectively. The BF-SSD algorithm improves the detection accuracy of tram obstacles, especially the detection ability of small target obstacles.