기계학습을 이용한 필터의 성능 저하 예측

• Published in: 한국CDE학회 논문집 Vol. 24; no. 3; pp. 268 - 279
• Main Authors: 소민섭(Min-Seop So), 신종호(Jong-Ho Shin)
• Format: Journal Article
• Language: Korean
• Published: (사)한국CDE학회 01.09.2019; 한국CDE학회
• Subjects: 기계공학; Condition based maintenance; Remaining useful life; Prognostics and health management; Product usage; Machine learning
• ISSN: 2508-4003; 2508-402X
• DOI: 10.7315/CDE.2019.268

Unexpected facility failures of heavy industry derive large cost so that it is important to prevent it beforehand by estimating machine health status, predicting remaining useful life (RUL), and determining proper time schedule of facility for inspection and replacement. Accordingly, active researches have been proceeding recently, such as prognostics and health management (PHM), condition based maintenance (CBM) method for failure prediction, and so on. The PHM and CBM are technologies that collect operating and environment data related to the failure/ lifetime of the equipment and predict failure in advance before failure. In accordance with this purpose, this study proposes a method to predict remaining useful life by collected operating and environment data. The methodology proposed in this study includes signal processing techniques to remove noise signal from sensor data, and clustering, support vector machine, and deep learning, which are used to calculate and predicting reduction rate according to usage environment. KCI Citation Count: 0