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서명교 ( Myung-kyo Seo ),윤원영 ( Won Young Yun ) 한국품질경영학회 2017 품질경영학회지 Vol.45 No.1
Purpose: Hot strip rolling mill consists of a lot of mechanical and electrical units. In condition monitoring and diagnosis phase, various units could be failed with unknown reasons. In this study, we propose an effective method to detect early the units with abnormal status to minimize system downtime. Methods: The early warning problem with various units is defined. K-means and PAM algorithm with Euclidean and Manhattan distances were performed to detect the abnormal status. In addition, an performance of the proposed algorithm is investigated by field data analysis. Results: PAM with Manhattan distance(PAM_ManD) showed better results than K-means algorithm with Euclidean distance(K-means_ED). In addition, we could know from multivariate field data analysis that the system reliability of hot strip rolling mill can be increased by detecting early abnormal status. Conclusion: In this paper, clustering-based monitoring and fault detection algorithm using Manhattan distance is proposed. Experiments are performed to study the benefit of the PAM with Manhattan distance against the K-means with Euclidean distance.
서명교(Myung Kyo Seo),홍완기(Wan Kee Hong),김종식(Jong Shik Kim) 대한기계학회 2002 대한기계학회 춘추학술대회 Vol.2002 No.10
To have high flatness quality of hot rolled strip in hot strip finishing mills, a new flatness control system is proposed in this paper. The proposed flatness control system is synthesized by the system identification and self-tuning PI control methods. It is founded that the self-tuning PI controller has better performances than the PI controller with fixed gains and than the minimum variance controller by the computer simulation.
윤명섭(Myung-Sup Yoon),이동혁(Dong-Hyuk Yi),윤원식(Won-Sik Yoon),서명교(Myung-Kyo Seo),유승엽(Seung-Yup Ryu) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.11
Supervised machine learning technique was applied to accurately predict the performance of the clean room air conditioner (CRAC) installed in the field. The performance of two neural networks was compared. One is the control group neural network using the laboratory sensor data and the other is the experimental group neural network using the product sensor data as an input. In both cases, they share laboratory performance results as an output label. Training data set of 2,816 combinations were acquired in the laboratory for the various indoor climate, outdoor climate and CRAC fan output conditions. When predicted with two trained ANNs, the control group showed better results thant the experimental group. In addition, the experimental group ANN performance prediction showed relatively more accurate results than the performance values calculated directly from the product sensors.
윤명섭(Myung-Sup Yoon),이동혁(Dong-Hyuk Yi),윤원식(Won-Sik Yoon),서명교(Myung-Kyo Seo),유승엽(Seung-Yup Ryu) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.11
Supervised machine learning technique was applied to accurately predict the performance of the clean room air conditioner (CRAC) installed in the field. The performance of two neural networks was compared. One is the control group neural network using the laboratory sensor data and the other is the experimental group neural network using the product sensor data as an input. In both cases, they share laboratory performance results as an output label. Training data set of 2,816 combinations were acquired in the laboratory for the various indoor climate, outdoor climate and CRAC fan output conditions. When predicted with two trained ANNs, the control group showed better results thant the experimental group. In addition, the experimental group ANN performance prediction showed relatively more accurate results than the performance values calculated directly from the product sensors.