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논문 기본 정보

자료유형
학술저널
저자정보
김강휘 (충남대학교) 강지훈 (한국산업기술대학교) 박승환 (충남대학교)
저널정보
Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Vol.38 No.4
발행연도
2021.4
수록면
269 - 277 (9page)
DOI
10.7736/JKSPE.021.002

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초록· 키워드

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A smart factory with Big Data analytics is getting attention because of its ability to automate and make the manufacturing environment more intelligent. At the same time, higher reliability is required with a drastic increase in complexity and uncertainty within the current system of manufacturing fields. The pump is considered as one of the most crucial equipment as it can affect the overall manufacturing performance of the manufacturing processes and it needs to be timely diagnosed of its mechanical condition as a top priority. In this research, we propose an operation system of centrifugal pumps and a data-driven fault diagnostic model that is developed by collecting relevant multivariate data from several natures. Proposed machine learning models can be used for detecting and diagnosing pump faults via analytical processes containing signal preprocessing and feature engineering procedures. Simulation and case studies from rotating machinery have demonstrated the effectiveness of the proposed analytical framework not only for attaining quantitative reliability but practical usages in actual manufacturing fields as well.

목차

1. 서론
2. Experimental Studies
3. Data Preprocessing
4. Machine Learning Model for Fault Diagnosis
5. 결론
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