所属单位：自动化学院
发表刊物：2017 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-HARBIN)
关键字：SEPIC converters Faults Diagnosis Fault Feature Particle Swarm Optimization(PSO) Deep Belief Network(DBN)
摘要：Effective fault diagnosis for mission-critical and safety-critical systems has been an essential and mandatory technique to reduce failure rate and to prevent unscheduled shutdown. A novel optimization deep belief network (DBN) in this study has been proposed for a closed-loop single-ended primary-inductance converter (SEPIC) fault diagnosis. Firstly, the wavelet packet decomposition technique is used to extract the energy values from 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, neuron numbers of the two hidden layers are selected by particle swarm optimization (PSO) algorithm and training data. Finally, eleven fault modes such as power MOSFET, inductance, diode and capacitor open circuit faults (OCFs) and short circuit faults (SCFs) are isolated by PSO-DBN. Compared with other intelligent diagnostic method such as back propagation neural network (BPNN) and support vector machine (SVM), experiment results show that the proposed method has a higher classification accuracy rate which means its effectiveness and superiority.
ISSN号：2166-5656
是否译文：否
发表时间：2017-01-01
合写作者：Sun, Quan,Jiang, Yuanyuan,Shao, Liwei,Chen, Donglei
通讯作者：王友仁
发表时间：2017-01-01