Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis
Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis
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DOI：
10.1109/tie.2014.2345331
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发表时间：
2015-03
影响因子：
7.7
通讯作者：
Shen Yin;Xiangping Zhu;O. Kaynak
中科院分区：
文献类型：
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作者：
Shen Yin;Xiangping Zhu;O. Kaynak
近二十年来，标准偏最小二乘法（PLS）成为大型流程工业关键绩效指标（KPI）监控的有力工具。然而，标准的方法及其最近的修改仍然遇到一些问题，故障诊断相关的KPI的底层过程。科普这些问题，本文提出了一种改进的PLS（IPLS）方法。IPLS能够将可测量的过程变量分别分解为KPI相关和不相关的部分。在此基础上，设计相应的测试统计量，以提供有意义的故障诊断信息，从而可以采取相应的维护措施，以确保系统的预期性能。为了证明所提出的方法的有效性，数值例子和田纳西州伊斯曼（TE）基准过程分别使用。可以看出，所提出的方法显示出令人满意的结果，不仅诊断KPI相关的故障，而且其高故障检测率。
Standard partial least squares (PLS) serves as a powerful tool for key performance indicator (KPI) monitoring in large-scale process industry for last two decades. However, the standard approach and its recent modifications still encounter some problems for fault diagnosis related to KPI of the underlying process. To cope with these difficulties, an improved PLS (IPLS) approach is presented in this paper. IPLS is able to decompose the measurable process variables into the KPI-related and unrelated parts, respectively. Based on it, the corresponding test statistics are designed to offer meaningful fault diagnosis information and thus, the corresponding maintenance actions can be further taken to ensure the desired performance of the systems. In order to demonstrate the effectiveness of the proposed approach, a numerical example and Tennessee Eastman (TE) benchmark process are respectively utilized. It can be seen that the proposed approach shows satisfactory results not only for diagnosing KPI-related faults but also for its high fault detection rate.