Application and Development of Intelligent Technology in High Speed Railway Signal System

2021 ◽  
Keyword(s):  
High Speed ◽  
Signal System ◽  
High Speed Railway ◽  
Railway Signal ◽  
Intelligent Technology

Real-Time Database System Implementation of Railway Signal Power Source Remote Monitoring

2011 ◽  
Vol 128-129 ◽  
pp. 961-964
Author(s):  
Zhi Jian Qu ◽  
Li Liu
Keyword(s):  
Power Supply ◽  
Remote Monitoring ◽  
High Speed ◽  
Low Voltage ◽  
Signal Power ◽  
High Speed Railway ◽  
Railway Traffic ◽  
Power Failure ◽  
Railway Signal ◽  
Real Time Database

Railway signal is a key technology for high-speed railway，which is the foundation to keep the high-speed train line. Railway signal power is the automatic blocking of railway lines and 10kV lines transform into 380V power through after the power supply for railway signals. Signal power as the railway traffic signal of the power supply, it belongs to the first level of power system load. Its 10kV high voltage side stik up by the major of electric, 380V low voltage side maintenance by the signal major. When the signal power failure, often occur shirk responsibilities between the different majors, in order to define the responsibilities of the accident better, which need automation remote monitoring for main railway lines of the railway signal power and scheduling control as soon as possible[1-3].

scholarly journals Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fan Gao ◽  
Fan Li ◽  
Zhifei Wang ◽  
Wenqi Ge ◽  
Xinqin Li
Keyword(s):  
Text Mining ◽  
High Speed ◽  
Single Layer ◽  
Feature Representation ◽  
Classification Model ◽  
High Speed Railway ◽  
Railway Signal ◽  
Representation Method ◽  
Multilevel Classification

In this paper, the multilevel classification model of high-speed railway signal equipment fault based on text mining technology is proposed for the data of high-speed railway signal fault. An improved feature representation method of TF-IDF is proposed to extract the feature of fault text data of signal equipment. In the multilevel classification model, the single-layer classification model was designed based on stacking integrated learning idea; the recurrent neural network BiGRU and BiLSTM were used as primary learners, and the weight combination calculation method was designed for secondary learners, and k-fold cross verification was used to train the stacking model. The multitask cooperative voting decision tree was designed to correct the membership relationship of classification results of each layer. Ten years of signal switch machine fault data of high-speed railway are used for experimental analysis; the experiment shows that the multilevel classification model can effectively improve the classification of signal equipment fault multilevel classification task evaluation index and can ensure the correctness of the subordinate relations’ classification results.

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