Online Process Monitoring Using Recursive Mutual Information-Based Variable Selection and Dissimilarity Analysis With No Prior Information
Online Process Monitoring Using Recursive Mutual Information-Based Variable Selection and Dissimilarity Analysis With No Prior Information
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使用基于递归互信息的变量选择和无先验信息的相异性分析进行在线过程监控
DOI：
10.1109/access.2018.2873806
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发表时间：
2018
期刊：
影响因子：
3.9
通讯作者：
Jun Liang
中科院分区：
文献类型：
--
作者：
Jing Zeng;Xiaoyi Luo;Jun Liang
传统的相异度(DISSIM)方法基于数据分布来挖掘潜在的故障特征，并且对过程的结构变化敏感。然而，由于非信息变量带来的噪声严重影响了系统的监测性能，尤其是在全厂范围的过程中，该模型没有发现哪些变量对所关注的故障有重要影响，其监测性能，包括故障检测和诊断性能也严重下降。由于互信息(MI)可以探索变量之间的线性和非线性相关性，本文提出了一种基于互信息的递归变量选择算法。该方法能有效地在线提取故障信息最丰富的变量，降低了计算复杂度。然后基于在线选择的变量，计算相异度指数来检测从正常状态到故障状态的分布变化，并提出了一种基于MI的诊断方法，以进一步调查故障的责任变量。通过变量选择，不仅可以用信息变量突出过程的局部特征，而且可以消除非信息变量的影响，从而自适应地更新控制限，显著提高监控性能的灵敏度和准确性。此外，基于MI的诊断方法利用高阶统计量探索选定变量的贡献，克服了变量方差带来的不足。田纳西州伊士曼(TE)基准流程的案例研究证明了该方法的可行性和有效性。
The traditional dissimilarity (DISSIM) method explores the underlying fault characteristic based on the data distribution and is sensitive to the structure change of the process. However, it fails to explore which variables are significant to the concerned faults and its monitoring performance, including fault detection and diagnosis performance, is seriously decreased by the noise brought by non-informative variables, especially in plant-wide process. Since mutual information (MI) can explore both the linear and nonlinear dependencies of variables, a recursive MI-based variable selection algorithm is proposed in this paper. It can efficiently extract the most informative variables to the faults online and reduce the computational complexity. Then based on the variables selected online, the dissimilarity index is calculated to detect the distribution changes from normal to a fault condition and an MI-based diagnosis method is developed to further investigate the responsible variables to the fault. With the variable selection, not only the local characteristic of the process can be highlighted by the informative variables but also the influence of the non-informative variables can be eliminated, thus the control limit can be adaptively updated and the sensitivity and accuracy of the monitoring performance can be significantly improved. Moreover, the MI-based diagnosis method explores the contribution of selected variables with a high-order statistic and overcomes shortage brought by variable variance. Case study on Tennessee Eastman (TE) benchmark process demonstrates the feasibility and efficiency of our method.
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影响因子：
4.9
作者：
Zhang, Yingwei;Wang, Fuli;Zhao, Chunhui
通讯作者：
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影响因子：
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DOI：
10.1016/j.chemolab.2014.01.009
发表时间：
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影响因子：
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