An Intelligent Fault Diagnosis Method Based on FastDTW for Railway Turnout
JI Wenjiang1, ZUO Yuan1, HEI Xinhong1, SEI Takahashi2, HIDEO Nakamura2
1. Faculty of Computer Science and Engineering,Xi′an University of Technology,Xi′an 710048; 2. Department of Computer Engineering,Nihon University,Funabashi 274-8501; 3. Graduate School of Frontier Science,The University of Tokyo,Tokyo 113-8656
Abstract：The turnout handles the direction of the train.It is a key equipment for the safety of railway transportation system.An intelligent fault diagnosis method based on fast dynamic time warping(FastDTW) for railway turnout is proposed in this paper.It is testified by the real action current data obtained from switch machine model No.ZD7.Firstly,the original current curve is segmented according to wave form features.Then,the warp path distance between the standard sample and the tested current curve is obtained by FastDTW algorithm.Finally,a dynamic optimized threshold is exploited to confirm whether there is a fault in the turnout.The experimental results show the proposed method works well with both single and double action type turnout machines with only 200 turnout action current samples.The proposed method is suitable for the train control system of new generation as well due to its high diagnosis accuracy and low time cost.
姬文江, 左元, 黑新宏, 高橋聖, 中村英夫. 基于FastDTW的道岔故障智能诊断方法[J]. 模式识别与人工智能, 2020, 33(11): 1013-1022.
JI Wenjiang, ZUO Yuan, HEI Xinhong, SEI Takahashi, HIDEO Nakamura. An Intelligent Fault Diagnosis Method Based on FastDTW for Railway Turnout. , 2020, 33(11): 1013-1022.
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