Degradation state mining and identification for railway point machines

Chong Bian (1) , Shunkun Yang (2) , Tingting Huang (2) , Qingyang Xu , Jie Liu (2) , Enrico Zio (3)

1 School of Automation and Electrical Engineering [Beijing] (University of Science and Technology Beijing)
2 School of Reliability and Systems Engineering [Beijing]
3 LGI - Laboratoire Génie Industriel - EA 2606

Critical point machine failure in railway-signal systems can lead to fatal accidents. Hence, early identification of anomalies is vital in guaranteeing reliable and safe transportation. However, most of the existing early fault diagnosis methods can only estimate the degradation trend under a specific fault mode. How to analyze the diversified degradation conditions under multiple fault modes is still a key problem. Considering the diversity of fault modes, this study proposes an early fault diagnosis 2 method based on self-organizing feature map network and support vector machine, focusing on the use of non-fault data to simultaneously mine and accurately identify degradation states under different fault modes, to provide guidance for proactive machine maintenance. The experimental results obtained via application of this scheme to field data for railway point machines demonstrate that the proposed methodology can effectively mine and accurately identify degradation states with different machine characteristics.

Mots clés: Degradation state mining; degradation state identification; early fault diagnosis; railway point machine; self-organizing mapping; support vector machine

Domaines: Informatique [cs]

Chong Bian, Shunkun Yang, Tingting Huang, Qingyang Xu, Jie Liu, et al.. Degradation state mining and identification for railway point machines. Reliability Engineering and System Safety, 2019, 188, pp.432-443. ⟨10.1016/j.ress.2019.03.044⟩. ⟨hal-02428541⟩