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.

1. 서 론
2. 데이터 및 연구방법
    2.1 데이터 설명 및 전처리
    2.2 LASSO Regression
    2.3 주성분 이상 시점진단
    2.4 머신러닝 이미지 분류 CNN
3. 실증분석 결과
    3.1 주요신호 탐색
    3.2 주성분 이상 시점 진단
    3.3 CNN 이미지 분류 활용
4. 결 론
References

• Sehyug Kwon(한남대학교 비즈니스통계학과) | 권세혁 Corresponding Author