Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 9, Issue 4, Pages 2239-2247Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2012.2214394
Keywords
Data-driven; hot strip mill; key performance indicator (KPI); prediction and diagnosis
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Funding
- EC Project PAPYRUS [257580]
- National Natural Science Foundation of China [61074085]
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In this paper, a data-driven scheme of key performance indicator (KPI) prediction and diagnosis is developed for complex industrial processes. For static processes, a KPI prediction and diagnosis approach is proposed in order to improve the prediction performance. In comparison with the standard partial least squares (PLS) method, the alternative approach significantly simplifies the computation procedure. By means of a data-driven realization of the so-called left coprime factorization (LCF) of a process, efficient KPI prediction, and diagnosis algorithms are developed for dynamic processes, respectively, with and without measurable KPIs. The proposed KPI prediction and diagnosis scheme is finally applied to an industrial hot strip mill, and the results demonstrate the effectiveness of the proposed scheme.
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