기계학습 알고리즘 기반의 가스정압기 이상상태 진단에 대한 연구
Study on Fault Detection of a Gas Pressure Regulator Based on Machine Learning Algorithms
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초록
본 논문에서는 정압기의 이상 상태 진단을 위한 기계학습 방법을 제안한다. 일반적으로 설비의이상 상태 탐지를 위한 기계학습 모델 구현에는 관련 센서의 설치와 데이터 수집 과정이 동반되나, 정압기는 설비 특성상 안전문제에 매우 민감하여 추가적인 센서 설치가 매우 까다롭다. 이에본 논문에서는 센서의 추가 설치 없이 정압기 설비에서 자체 수집되는 유량과 유압 데이터만을가지고 정압기의 이상 상태를 조기에 판단하는 기계학습 모델을 제안한다. 본 논문에서는 정압기의 비정상데이터가 충분하지 않은 관계로, 모델 학습 시 오버 샘플링(Over-Sampling)을 적용하여모델이 모든 클래스에 균형적으로 학습하도록 하였다. 또한, 그레이디언트 부스팅(Gradient Boosting), 1차원 합성곱 신경망(1D Convolutional Neural Networks), LSTM(Long Short-Term Memory) 등의 기계학습 알고리즘을 적용하여 정압기의 이상 상태를 판단하는 분류모델을 구현하였고, 실험 결과 그레이디언트 부스팅 알고리즘이 정확도 99.975%로 가장 성능이 우수함을 확인하였다.
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.
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