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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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 311; S. 1 - 10
Hauptverfasser: Xu, Lin, Cao, Maoyong, Song, Baoye, Zhang, Jiansheng, Liu, Yurong, Alsaadi, Fuad E.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 15.10.2018
Schlagworte:
Deep neural network
Fault diagnosis
Feature extraction
Power rectifier
Sparse autoencoder
Fault diagnosis
Power rectifier
Feature extraction
Deep neural network
Sparse autoencoder
ISSN:0925-2312, 1872-8286
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Beschreibung
Zusammenfassung: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2018.05.040