Open-circuit fault diagnosis of power rectifier using sparse autoencoder based deep neural network
This paper is concerned with the open-circuit fault diagnosis of phase-controlled three-phase full-bridge rectifier by using a sparse autoencoder-based deep neural network (SAE-based DNN). Firstly, some preliminaries on SAE-based DNN are briefly introduced to automatically learn the representative f...
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| Vydané v: | Neurocomputing (Amsterdam) Ročník 311; s. 1 - 10 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
15.10.2018
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | This paper is concerned with the open-circuit fault diagnosis of phase-controlled three-phase full-bridge rectifier by using a sparse autoencoder-based deep neural network (SAE-based DNN). Firstly, some preliminaries on SAE-based DNN are briefly introduced to automatically learn the representative fault features from the raw fault signals. Then, a novel strategy is developed to design the structure of the SAE-based DNN, by which the depth and hidden neurons of the SAE-based DNN could be regularly determined to extract the features of input signals. Furthermore, the fault model and system framework are presented to diagnose the open-circuit fault of the three-phase full-bridge rectifier. Finally, the effectiveness of the developed novel strategy is verified by the results of simulation experiments, and the superiority of the novel SAE-based DNN is evaluated by comparing with other frequently used approaches. |
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| AbstractList | This paper is concerned with the open-circuit fault diagnosis of phase-controlled three-phase full-bridge rectifier by using a sparse autoencoder-based deep neural network (SAE-based DNN). Firstly, some preliminaries on SAE-based DNN are briefly introduced to automatically learn the representative fault features from the raw fault signals. Then, a novel strategy is developed to design the structure of the SAE-based DNN, by which the depth and hidden neurons of the SAE-based DNN could be regularly determined to extract the features of input signals. Furthermore, the fault model and system framework are presented to diagnose the open-circuit fault of the three-phase full-bridge rectifier. Finally, the effectiveness of the developed novel strategy is verified by the results of simulation experiments, and the superiority of the novel SAE-based DNN is evaluated by comparing with other frequently used approaches. |
| Author | Song, Baoye Alsaadi, Fuad E. Cao, Maoyong Liu, Yurong Xu, Lin Zhang, Jiansheng |
| Author_xml | – sequence: 1 givenname: Lin surname: Xu fullname: Xu, Lin organization: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China – sequence: 2 givenname: Maoyong surname: Cao fullname: Cao, Maoyong email: my-cao@263.net organization: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China – sequence: 3 givenname: Baoye orcidid: 0000-0003-1631-5237 surname: Song fullname: Song, Baoye email: songbaoye@gmail.com organization: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China – sequence: 4 givenname: Jiansheng surname: Zhang fullname: Zhang, Jiansheng organization: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China – sequence: 5 givenname: Yurong surname: Liu fullname: Liu, Yurong organization: Department of Mathematics, Yangzhou University, Yangzhou 225002, China – sequence: 6 givenname: Fuad E. surname: Alsaadi fullname: Alsaadi, Fuad E. organization: Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
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| Keywords | Fault diagnosis Power rectifier Feature extraction Deep neural network Sparse autoencoder |
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| Title | Open-circuit fault diagnosis of power rectifier using sparse autoencoder based deep neural network |
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