A Novel Fault Diagnostic Approach for DC-DC Converters Based on CSA-DBN

• Published in: IEEE access Vol. 6; pp. 6273 - 6285
• Main Authors: Sun, Quan, Wang, Youren, Jiang, Yuanyuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Back propagation networks; Belief networks; Capacitors; Circuit faults; Converters; Crow search algorithm; dc-dc power converter; DC-DC power converters; deep belief network; Diagnostic systems; Energy value; Failure rates; Fault diagnosis; Faults; feature extraction; Inductance; MOSFETs; Neural networks; Neurons; Optimization; Safety critical; Search algorithms; Short circuits; Shutdowns; Support vector machines; System effectiveness; Voltage converters (DC to DC); wavelet packets
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2786458

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.