머신러닝 기법을 활용한 대용량 시계열 데이터 이상 시점탐지 방법론

• 영문명: Anomaly Detection of Big Time Series Data Using Machine Learning
• 발행기관: 한국산업경영시스템학회
• 저자명: 권세혁(Sehyug Kwon)
• 간행물 정보: 『산업경영시스템학회지』제43권 제2호, 33~38쪽, 전체 6쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2020.06.30

영문 초록

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

목차

1. 서 론

2. 데이터 및 연구방법

3. 실증분석 결과

4. 결 론

References

키워드

Anomaly Detection Convolution Neural Network LASSO Machine Learning Principal Components Analysis