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标题：Fault Diagnosis of Three-phase Full-bridge Rectifier Circuit based on Deep Neural Network
作者：Song, Baoye ;Kang, Changping ;Xu, Lin ;Zhang, Jiansheng
作者机构：[Song, Baoye ;Kang, Changping ;Xu, Lin ;Zhang, Jiansheng ] College of Electrical Engineering and Automation, Shandong University of Science and Techno 更多
会议名称：2018 Chinese Automation Congress, CAC 2018
会议日期：November 30, 2018 - December 2, 2018
来源：Proceedings 2018 Chinese Automation Congress, CAC 2018
出版年：2019
页码：468-473
DOI：10.1109/CAC.2018.8623247
摘要：This paper is concerned with the diagnostic issue of the open-circuit fault for the three-phase full-bridge rectifier based on an autoencoder-based deep neural network (AE-DNN). Firstly, the preliminary of the AE-DNN is briefly introduced. Then, the fault diagnosis model is presented for the open-circuit fault of the three-phase full-bridge rectifier. Finally, the superiority and effectiveness of the AE-DNN based fault diagnostic system is verified by several simulation experiments. It is concluded that the AE-DNN based fault diagnostic system can automatically extract and learn the features from the raw fault data and perform more robust ability to the noisy signals.
© 2018 IEEE.
收录类别：EI
资源类型：会议论文

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