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http://dx.doi.org/10.21289/KSIC.2020.23.5.825

Fault Diagnosis Algorithm of Electronic Valve using CNN-based Normalized Lissajous Curve  

Park, Seong-Mi (Dept. of Lift Engineering, Korea Lift College)
Ko, Jae-Ha (Green Energy Institute, Energy Innovative Industry R&D Department)
Song, Sung-Geun (Korea Electronics Technology Institute, Energy Conversion Research Center)
Park, Sung-Jun (Dept. Electrical Engineering, Chonnam National University)
Son, Nam Rye (Dept. of Information & Communication Engineering, Honam University)
Publication Information
Journal of the Korean Society of Industry Convergence / v.23, no.5, 2020 , pp. 825-833 More about this Journal
Abstract
Currently, the K-Water uses various valves that can be remotely controlled for optimal water management. Valve system fault can be classified into rotor defects, stator defects, bearing defects, and gear defects of induction motors. If the valve cannot be operated due to a gear fault, the water management operation can be greatly affected. For effective water management, there is an urgent need for preemptive repairs to determine whether gear is damaged through failure prediction diagnosis.. Recently, deep learning algorithms are being applied for valve failure diagnosis. However, the method currently applied has a disadvantage of attaching a vibration sensor to the valve. In this paper, propose a new algorithm to determine whether a fault exists using a convolutional neural network (CNN) based on the voltage and current information of the valve without additional sensor mounting. In particular, a normalized Lisasjous diagram was used to maximize the fault classification performance in the CNN-based diagnostic system.
Keywords
Convolutional Neural Network; Electronic Valve; Fault Diagnosis; Lissajous; Normalization;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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