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http://dx.doi.org/10.9708/jksci.2020.25.04.019

Study on Fault Detection of a Gas Pressure Regulator Based on Machine Learning Algorithms  

Seo, Chan-Yang (Postech Institute of Artificial Intelligence, POSTECH)
Suh, Young-Joo (Dept. of Computer Science and Engineering, POSTECH)
Kim, Dong-Ju (Postech Institute of Artificial Intelligence, POSTECH)
Publication Information
Journal of the Korea Society of Computer and Information / v.25, no.4, 2020 , pp. 19-27 More about this Journal
Abstract
In this paper, we propose a machine learning method for diagnosing the failure of a gas pressure regulator. Originally, when implementing a machine learning model for detecting abnormal operation of a facility, it is common to install sensors to collect data. However, failure of a gas pressure regulator can lead to fatal safety problems, so that installing an additional sensor on a gas pressure regulator is not simple. In this paper, we propose various machine learning approach for diagnosing the abnormal operation of a gas pressure regulator with only the flow rate and gas pressure data collected from a gas pressure regulator itself. Since the fault data of a gas pressure regulator is not enough, the model is trained in all classes by applying the over-sampling method. The classification model was implemented using Gradient boosting, 1D Convolutional Neural Networks, and LSTM algorithm, and gradient boosting model showed the best performance among classification models with 99.975% accuracy.
Keywords
Fault Detection; Gas Pressure Regulator; Gradient Boosting; Long Short-Term Memory(LSTM); 1D Convolutional Neural Networks; Over-Sampling;
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Times Cited By KSCI : 9  (Citation Analysis)
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