Influence of Contact Condition Between Rails and Wheels upon Track Circuit Shunting. 1st Report Quantitative Evaluation of Factors that cause Inadequate Track Circuit Shunting.

和彦, 永瀬; 俊明, 野村; 淳司, 平間

doi:10.1299/kikaic.63.4187

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“…Japanese scholars have studied the problem of the poor distribution of track circuits for nearly ten years and have conducted experiments [6]. The causes of shunt resistance under different environmental conditions have been summarized [7], and practical solutions have been given [8].…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Detection for ZPW-2000A Jointless Track Circuit Based on Deep Belief Network Optimized by Improved Particle Swarm Optimization Algorithm

2020

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With the rapid development of railway traffic, traffic safety has become a focus. The ZPW-2000A jointless track circuit is an important part of train control systems. Currently, the fault detection of the ZPW-2000A jointless track circuit still relies on the experience of maintenance personnel, which can introduce several problems, such as a low fault detection efficiency and large amounts of required labor. Although some artificial intelligence fault detection algorithms for the ZPW-2000A track circuit have been developed, their detection accuracy is not high enough to meet the needs of large-scale applications, and due to security requirements, the actual ZPW-2000A track circuit fault data cannot be directly obtained in large quantities. To solve these problems, an equivalent theoretical model of the Chinese ZPW-2000A jointless track circuit is proposed by using four-terminal network theory. Through this equivalent theoretical model, the original fault data were collected. Considering that the relationship between fault data and fault types of the ZPW-2000A jointless track circuit is not obvious, a deep belief network was designed to detect the fault modes of the ZPW-2000A jointless track circuit. In order to optimize the deep belief network performance, the particle swarm optimization algorithm optimized by the genetic algorithm (GAPSO) was selected to optimize the deep belief network. The simulation experiments indicated that the optimized deep belief network could achieve a 98.5% fault detection accuracy and a 98.6% F1 Score rate, which showed that the deep belief network optimization by the particle swarm optimization algorithm which was optimized by the genetic algorithm (GAPSO-DBN) model proposed in this paper, had high accuracy and robustness. The results show that it had higher accuracy and robustness than other fault detection methods, and it can greatly improve the level of ZPW-2000A track circuit fault detection in the future. INDEX TERMS ZPW-2000A jointless track circuit, deep belief network, fault detection, particle swarm optimization, four-terminal network, genetic algorithm.

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Section: Introductionmentioning

confidence: 99%