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
通讯作者:Zhang, Guohua
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)
出版年:2018
卷:11320 LNAI
页码:98-103
DOI:10.1007/978-3-030-03991-2_10
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.
© Springer Nature Switzerland AG 2018.
收录类别:EI
资源类型:会议论文
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