标题：Intelligent fault detection of high-speed railway turnout based on hybrid deep learning
作者：Zhuang, Zhi ;Zhang, Guohua ;Dong, Wei ;Sun, Xinya ;Wang, Chuanjiang
作者机构：[Zhuang, Zhi ;Wang, Chuanjiang ] Shandong University of Science and Technology, Qingdao, China;[Sun, Xinya ] Department of Automation, Tsinghua Univer 更多
会议名称：31st Australasian Joint Conference on Artificial Intelligence, AI 2018
会议日期：December 11, 2018 - December 14, 2018
来源：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
摘要：With the purpose of detecting the turnout fault without label data and fault data timely, this paper proposes a hybrid deep learning framework com-bining the DDAE (Deep Denoising Auto-encoder) and one-class SVM (Support Vector Machine) for turnout fault detection only using normal data. The proposed method achieves an accuracy of 98.67% on the real turn-out dataset for current curve, which suggests that this work realizes the purpose of detecting the fault with only normal data and provides a basis for the intelligent fault detection of turnouts.
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