文章基本信息

标题：Robust Just-in-time Learning Approach and Its Application on Fault Detection
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作者：Han Yu ; Shen Yin ; Hao Luo 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：15277-15282
DOI：10.1016/j.ifacol.2017.08.2437
语种：English
出版社：Elsevier
摘要：AbstractIn data-driven state estimation and process monitoring, the correctness of results mainly rely on the accuracy of measurement. Actually, noises, outliers and measured errors always exist in real industrial systems. Just-in-time learning (JITL) is an useful on-line learning method and can be applied for data-based state estimation. Due to the reality of inaccurate measurement, an improved JITL method with strengthen robustness is necessary to be studied. In this paper, a robust version of just-in-time learning strategy is proposed. It is inspired from the leverage weight. By calculating the leverage impact, the outliers in high leverage cases are treated to reduce their weight and affect less on output prediction. A typical nonlinear system experiment is employed to prove the robust and veracity of the proposed strategy. Finally, the robust JITL is implemented for fault detection on a three-tank system to verify its applicability.
关键词：KeywordsRobustjust-in-time learningdata drivenstate estimationfault detectionnonlinear systems