Study on Fault Detection of a Gas Pressure Regulator Based on Machine Learning Algorithms (기계학습 알고리즘 기반의 가스정압기 이상상태 진단에 대한 연구)

SEO CHANYANG (서찬양); Suh Young Joo (서영주); Dong-Ju Kim (김동주)

doi:10.9708/jksci.2020.25.04.019

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

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2020, 25(4), pp.19-27
DOI : 10.9708/jksci.2020.25.04.019
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : February 6, 2020
Accepted : April 3, 2020
Published : April 30, 2020

SEO CHANYANG ¹, Suh Young Joo ¹, Dong-Ju Kim ¹

¹포항공과대학교

Accredited

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, 1D Convolutional Neural Networks, Over-Sampling

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

ABSTRACT

KEYWORDS

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REFERENCES (19) * References for papers published after 2023 are currently being built.

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* References for papers published after 2023 are currently being built.