• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 31 Issue 4
Jul.  2018
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Article Contents
ZHONG Zhiwang, CHEN Jianyi, TANG Tao, XU Tianhua, WANG Feng. SVDD-Based Research on Railway-Turnout Fault Detection and Health Assessment[J]. Journal of Southwest Jiaotong University, 2018, 53(4): 842-849. doi: 10.3969/j.issn.0258-2724.2018.04.024
Citation: ZHONG Zhiwang, CHEN Jianyi, TANG Tao, XU Tianhua, WANG Feng. SVDD-Based Research on Railway-Turnout Fault Detection and Health Assessment[J]. Journal of Southwest Jiaotong University, 2018, 53(4): 842-849. doi: 10.3969/j.issn.0258-2724.2018.04.024

SVDD-Based Research on Railway-Turnout Fault Detection and Health Assessment

doi: 10.3969/j.issn.0258-2724.2018.04.024
  • Received Date: 31 Oct 2017
  • Publish Date: 01 Aug 2018
  • In order to improve the quality of railway-turnout maintenance procedures and reduce equipment failure, a method for turnout-fault-detection and health-index evaluation based on the support vector domain description (SVDD) technique, is proposed. Through analysis of the switch-equipment mechanism, the turnout power curve was divided into 3 parts, each representing the unlocking, conversion, and locking processes respectively. To this end, characteristic parameters of the switch power curve were extracted. Subsequently, in the feature space, an SVDD-based turnout-fault-detection algorithm and health assessment were proposed. Lastly, the efficiency of the proposed algorithm was verified by comparing simulation results against experimental data. Experimental results demonstrate that the proposed health index possesses an accuracy of 95%, which could prove to be significant in on-site maintenance and guidance applications.

     

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