Abstract
Steel markets are very competitive and demand greater gauge precision and higher production rates. These growing requirements result in tandem rolling mill, which is of substantial interest to the steel industry, in order to improve quality and productivity. In such an environment, it is important to construct appropriate condition monitoring, which can lead to achieving the highest economic efficiency and avoiding equipment damage. This paper proposes a comprehensive condition monitoring methodology based on statistical feature extraction technique to increase the efficiency of feature extraction from high-dimensional feature space. It is examined that one can explore easily the effective features by using three-dimensional feature space for the condition monitoring. The method has been applied on condition monitoring of the stationary rolling in steel industry.
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Oh, JS., Kim, HE. (2015). A Case Study on Condition Monitoring Based on Statistical Feature for Coil Break on Tandem Cold Rolling. In: Lee, W., Choi, B., Ma, L., Mathew, J. (eds) Proceedings of the 7th World Congress on Engineering Asset Management (WCEAM 2012). Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-06966-1_43
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DOI: https://doi.org/10.1007/978-3-319-06966-1_43
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