《计算机应用研究》|Application Research of Computers

基于概率密度PCA的多模态过程故障检测

PCA based on probability density for fault detection of multimodal processes

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作者 郭金玉,刘玉超,李元
机构 沈阳化工大学 信息工程学院,沈阳 110142
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文章编号 1001-3695(2019)05-024-1396-04
DOI 10.19734/j.issn.1001-3695.2017.12.0838
摘要 为了提高故障检测和分类能力,提出基于概率密度PCA的多模态过程故障检测算法。对各模态的训练数据建立PCA模型,计算各个模型的控制限和匹配系数。根据匹配系数计算各模态统一的控制限。对新来的数据,运用概率密度确定其模态。新来数据向对应模态的模型上投影并计算统一的统计量,比较统计量与控制限进行多模态过程故障检测。把该方法应用到数值例子和半导体过程中,仿真结果表明,该算法在分类及多模态过程故障检测方面具有很高的准确性。
关键词 多模态过程; 故障检测; 概率密度; 主元分析; 匹配系数
基金项目 国家自然科学基金重大项目(61490701)
国家自然科学基金资助项目(61174119)
辽宁省教育厅重点实验室项目(LZ2015059)
辽宁省自然科学基金资助项目(201602584)
辽宁省教育厅项目(L2016007,L2015432)
本文URL http://www.arocmag.com/article/01-2019-05-024.html
英文标题 PCA based on probability density for fault detection of multimodal processes
作者英文名 Guo Jinyu, Liu Yuchao, Li Yuan
机构英文名 College of Information Engineering,Shenyang University of Chemical Technology,Shenyang 110142,China
英文摘要 In order to improve the ability of fault detection and classification, this paper proposed PCA based on probability density for fault detection of multimodal processes. It established PCA model for training data of each mode, and calculated the control limits and matching coefficients of each model. It calculated the unified control limit of each mode according to the matching coefficients. For a new data, it determined its mode by the probability density. It projected the new data to PCA model of the corresponding mode and calculated the unified statistics. It performed fault detection of multimodal processes by comparing the statistics with control limit. This paper applied the method to a numerical example and the semiconductor process. Simulation results show that the proposed algorithm has high accuracy in classification and fault detection of multimodal processes.
英文关键词 multimodal processes; fault detection; probability density; principal component analysis(PCA); matching coefficients
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收稿日期 2017/12/5
修回日期 2018/1/23
页码 1396-1399,1408
中图分类号 TP277
文献标志码 A