Canonical variate analysis-based contributions for fault identification
Canonical variate analysis-based contributions for fault identification
复制标题
基于典型变量分析的故障识别贡献
DOI：
10.1016/j.jprocont.2014.12.001
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发表时间：
2015-02-01
影响因子：
4.2
通讯作者：
Braatz, Richard D.
中科院分区：
文献类型：
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作者：
Jiang, Benben;Huang, Dexian;Braatz, Richard D.
虽然典型变量分析（CVA）已经被用作降维技术以考虑过程数据中的序列相关性与系统动态，但是其在故障识别中的有效性（即，与故障最密切相关的变量的识别）尚未被广泛研究。本文提出了基于CVA-based故障识别的贡献，其中两种类型的贡献开发的基础上，在规范的状态空间和残差空间中的变化。这两个贡献被用来分类故障变量为状态空间故障变量（SSFV）和剩余空间故障变量（RSFV），这提高了对每个故障的特性的理解，以及基于不同的统计故障监测的性能。田纳西伊士曼过程的有效性证明所提出的方法。仿真结果表明，故障变量识别的基于CVA-based的贡献，可以影响状态空间的统计，剩余空间，或两者兼而有之;和异常事件被观察到更经常被链接到故障变量的剩余空间，而不是在状态空间。(C)2014爱思唯尔有限公司版权所有。
While canonical variate analysis (CVA) has been used as a dimensionality reduction technique to take into account serial correlations in the process data with system dynamics, its effectiveness in fault identification (i.e., identification of variables most closely associated with a fault) in industrial processes has not been extensively investigated. This paper proposes CVA-based contributions for fault identification, where two types of contributions are developed based on the variations in the canonical state space and in the residual space. The two contributions are used to categorize faulty variables into state-space faulty variables (SSFVs) and residual-space faulty variables (RSFVs), which enhances the understanding of the character of each fault as well as the performance of fault monitoring based on different statistics. The effectiveness of the proposed approach is demonstrated on the Tennessee Eastman process. The simulation results show that the faulty variables identified by the CVA-based contributions can impact the statistics of the state space, the residual space, or both; and abnormal events are observed to be more often linked to faulty variables in the residual space rather than in the state space. (C) 2014 Elsevier Ltd. All rights reserved.