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

ZHONG Zhiwang; CHEN Jianyi; TANG Tao; XU Tianhua; WANG Feng

doi:10.3969/j.issn.0258-2724.2018.04.024

Volume 31 Issue 4

Jul. 2018

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Journal of Southwest Jiaotong University > 2018 > 53(4): 842-849.

ZHOU Ning, ZHANG Weihua, WANG Dong. Lumped Mass Model for Dynamic Performance Simulation of Pantograph[J]. Journal of Southwest Jiaotong University, 2011, 24(3): 398-403. doi: 10.3969/j.issn.0258-2724.2011.03.007

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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

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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

Abstract

Abstract

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.
- railway turnouts,
- SVDD,
- fault detection,
- health evaluation

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References

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

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