所属单位：自动化学院
发表刊物：IEEE ACCESS
关键字：Crow search algorithm dc-dc power converter deep belief network fault diagnosis feature extraction wavelet packets
摘要：Effective fault diagnosis for mission-critical and safety-critical systems has been an essential and mandatory technique to reduce failure rate and prevent unscheduled shutdown. In this paper, to realize fault diagnosis for a closed-loop single-ended primary inductance converter, a novel optimization deep belief network (DBN) is presented. First, wavelet packet decomposition is adopted to extract the energy values from the voltage signals of four circuit nodes, as the fault feature vectors. Then, a four-layer DBN architecture including input and output layers is developed. Meanwhile, the number of neurons in the two hidden layers is selected by the crow search algorithm (CSA) with training samples. Not only the hard faults such as open-circuit faults and short-circuit faults but also the soft faults such as the component degradation of power MOSFET, inductor, diode, and capacitor are considered in this study. Finally, these fault modes are isolated by CSA-DBN. Compared with the back-propagation neural network and support vector machine fault diagnosis methods, both simulation and experimental results show that the proposed method has a higher classification accuracy that proves its effectiveness and superiority to the other methods.
ISSN号：2169-3536
是否译文：否
发表时间：2018-01-01
合写作者：Sun, Quan,Jiang, Yuanyuan
通讯作者：王友仁
发表时间：2018-01-01