정보처리학회 논문지 (The Transactions of the Korea Information Processing Society)

한국정보처리학회 (Korea Information Processing Society)
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설비 결함 식별 최적화를 위한 오토인코더 기반 N 분할 주파수 영역 이상 탐지

Autoencoder Based N-Segmentation Frequency Domain Anomaly Detection for Optimization of Facility Defect Identification

  • 박기창 ((주)리쉐니에 제조지능화기술연구소 ) ;
  • 이용관 (한국공학대학교 그랜드ICT연구센터)
  • 투고 : 2023.03.10
  • 심사 : 2024.02.26
  • 발행 : 2024.03.31
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초록

제조 분야 설비 예지보전을 위해서 진동, 전류, 온도 등 물리 데이터를 기반으로 설비 이상을 탐지하는 인공지능 학습 모델이 활용되고 있다. 설비 결함, 고장 등 설비 이상 유형은 매우 다양하므로, 주로 오토인코더 기반 비지도 학습 모델을 이용한 이상 탐지 방법이 적용되고 있다. 설비 상태의 정상, 비정상 여부는 오토인코더의 재구성 오차를 이용해 효과적으로 분류할 수 있지만, 설비 이상의 구체적인 상태를 식별하는 데 한계가 있다. 설비 불균형, 정렬 불량, 고정 불량 등 설비 이상 상황 발생 시, 설비 진동 주파수는 특정 영역에서 정상 상태와 다른 패턴을 나타낸다. 본 논문에서는 전체 진동 주파수 범위를 N개 영역으로 나누어 이상 탐지를 수행하는 N 분할 이상 탐지 방법을 제시하였다. 압축기의 진동 데이터를 이용해 주파수와 강도를 달리한 9종의 이상 데이터를 대상으로 실험한 결과, N 분할을 적용하였을 때 더 높은 이상 탐지 성능을 나타냈다. 제안 방법은 설비 이상 탐지 이후, 설비 이상 구체화에 활용될 수 있다.

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

키워드

과제정보

이 논문은 과학기술정보통신부의 재원으로 정보통신기획평가원의 지원을 받아 수행된 지역지능화혁신인재양성사업(IITP-2024-2020-0-01741, 50%)과 중소벤처기업부의 재원으로 중소기업기술정보진흥원의 지원을 받아 수행된 스마트제조혁신 R&D 지원사업 연구 결과로 수행되었음(RS-2022-00141076, 50%).

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