Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor.
Detecting replacement conditions of railway point machines is important to simultaneously satisfy the budget-limit and train-safety requirements. In this study, we consider classification of the subtle differences in the aging effect-using electric current shape analysis-for the purpose of replacement condition detection of railway point machines. After analyzing the shapes of after-replacement data and then labeling the shapes of each before-replacement data, we can derive the criteria that can handle the subtle differences between "does-not-need-to-be-replaced" and "needs-to-be-replaced" shapes. On the basis of the experimental results with in-field replacement data, we confirmed that the proposed method could detect the replacement conditions with acceptable accuracy, as well as provide visual interpretability of the criteria used for the time-series classification.
Department of Computer and Information Science, Korea University, Sejong 30019, Korea. sjwon92@korea.ac.kr.
Jaewon
J
Sa
Department of Computer and Information Science, Korea University, Sejong 30019, Korea. ycc4477@korea.ac.kr.
Younchang
Y
Choi
Department of Computer and Information Science, Korea University, Sejong 30019, Korea. ychungy@korea.ac.kr.
Yongwha
Y
Chung
Department of Applied Statistics, Korea University, Sejong 30019, Korea. starkim@korea.ac.kr.
Hee-Young
HY
Kim
Department of Computer and Information Science, Korea University, Sejong 30019, Korea. dhpark@korea.ac.kr.
Daihee
D
Park
Sehwa R&D Center, Techno 2-ro, Yuseong-gu, Daejeon 34026, Korea. shy5406@hanmail.net.
Sukhan
S
Yoon
electric current shape analysis; maintenance engineering; railway point machine; replacement condition monitoring