文章基本信息

标题：Data-driven Key Performance Indicator Fault Detection Approach Based on Sparse Direct Orthogonalization
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作者：Hao Zhou ; Hao Ye ; Shen Yin 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：11620-11625
DOI：10.1016/j.ifacol.2020.12.643
语种：English
出版社：Elsevier
摘要：AbstractIn recent years, key performance indicator (KPI) detection has attracted much attention in large-scale process plants. Several methods have been developed to solve this issue. However, further studies find that post-processing methods have relatively high false alarm rates (FARs) for quality-unrelated faults. Also, methods combined with preprocessing, like orthogonal signal correction-modified partial least squares (OSC-MPLS), sometimes lack robustness. To deal with this problem, this paper proposes an enhanced pretreatment method, namely sparse direct orthogonalization (SDO), and a novel KPI-related fault detection approach called SDO-MPLS is developed. Compared with OSC-MPLS, the proposed approach has more robust performance and better interpretability, while a numerical case and the Tennessee Eastman process (TEP) are used to demonstrate the effectiveness of the proposed approach.
关键词：KeywordsKey performance indicatorsProcess monitoringSparse direct orthogonalizationModified partial least squaresFault detection