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.