Advertisement

The Prediction of Delay Time Class Caused by CTCS-3 Onboard System Fault Based on Decision Tree

  • Lijuan ShiEmail author
  • Ang Li
  • Liquan Chen
Conference paper
  • 18 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 617)

Abstract

The faults of train control system will lead to delay, which will affect the operational efficiency of the railway network. In this paper, the decision tree algorithm (CART) is used to predict the delay time level caused by CTCS-3 On-board System Fault, which takes the location of train failure, the fault component of CTCS-3 on-board system, the fault phenomenon of CTCS-3 on-board system as data features. In the natural language fault record, based on expert experience, extract the key features needed and grade the delay time. The selected features are put into the decision tree algorithm for classification and prediction, SMOTE algorithm is used to solve the problem of unbalanced number of categories, and grid search algorithm is used to adjust the model parameters. Finally, the output results of the algorithm are analyzed. The decision tree model yields a classification accuracy of 76% for the given data of fault feature and can be considered for delay time level prediction caused by CTCS-3 system fault. From the experimental results, the proposed method can be recommended for the prediction of the delay time level caused by CTCS-3 system fault.

Keywords

CTCS-3 on-board system fault Delay time class Expert knowledge Decision tree 

Notes

Acknowledgements

Authors would like to acknowledge the support of the research program of Comprehensive Support Technology for Railway Network Operation (2018YFB1201403), which is a subproject of Advanced Railway Transportation Special Project belonging to the 13th Five-Year National Key Research and Development Plan funded by Ministry of Science and Technology of China.

References

  1. 1.
    Lee WH, Yen LH, Chou CM (2016) A delay root cause discovery and timetable adjustment model for enhancing the punctuality of railway services. Transp Res Part C: Emerging Technol 73:49–64CrossRefGoogle Scholar
  2. 2.
    Guo W (2017) Research on train fault early warning method based on data Sigma completeness of train control systemGoogle Scholar
  3. 3.
    Xiao Q, Wang C, Liang H et al (2006) Application of C4.5 algorithm in train’s rail deformation detection. Comput Technol Develop 16(4)Google Scholar
  4. 4.
    Madhusudana CK, Kumar H, Narendranath S (2018) Fault diagnosis of face milling tool using decision tree and sound signal. Mater Today Proc 5(5):12035–12044CrossRefGoogle Scholar
  5. 5.
    Rabah B, Samir M (2018) Fault detection and diagnosis based on C4.5 decision tree algorithm for grid connected PV system. Solar Energy 173:610–634Google Scholar
  6. 6.
    Jiang Z, Wei D, Wang L et al (2018) Fault diagnosis of diesel engines based on a classification and regression tree (CART) decision tree. J Beijing Univ Chem Technol (Natural Science) 45(4):71–75Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Rail Infrastructure Durability and System SafetyTongji UniversityJiading District, ShanghaiPeople’s Republic of China
  2. 2.Key Laboratory of Road and Traffic Engineering of Ministry of EducationTongji UniversityJiading District, ShanghaiPeople’s Republic of China
  3. 3.China Railway Guangzhou Group Co., Ltd.GuangzhouChina

Personalised recommendations