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Affiliation of Author(s):自动化学院

Title of Paper:Fault Diagnosis of SEPIC Converters Based on PSO-DBN and Wavelet Packet Energy Spectrum

Journal:2017 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-HARBIN)

Key Words:SEPIC converters Faults Diagnosis Fault Feature Particle Swarm Optimization(PSO) Deep Belief Network(DBN)

Abstract: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 No.:2166-5656

Translation or Not:no

Date of Publication:2017-01-01

Co-author:Sun, Quan,Jiang, Yuanyuan,Shao, Liwei,Chen, Donglei

Correspondence Author:wang you ren