基于MLFDA的化工过程故障模式分类方法

doi:10.6040/j.issn.1672-3961.0.2017.181

山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 179-186.doi: 10.6040/j.issn.1672-3961.0.2017.181

基于MLFDA的化工过程故障模式分类方法

王磊,邓晓刚^*,曹玉苹,田学民

中国石油大学(华东)信息与控制工程学院, 山东青岛 266580

收稿日期:2017-04-18 出版日期:2017-10-20 发布日期:2017-04-18
通讯作者: 邓晓刚(1981— ),男,山东东营人,副教授,博士,主要研究方向为工业过程故障诊断技术. E-mail:dengxiaogang@upc.edu.cn E-mail:wangleipl@foxmail.com
作者简介:王磊(1992— ),男,山东滨州人,硕士研究生,主要研究方向为多模态过程故障诊断. E-mail:wangleipl@foxmail.com
基金资助:
国家自然科学基金资助项目(61403418,61273160,21606256);山东省自然科学基金资助项目(ZR2014FL016,ZR2016FQ21,ZR2016BQ14);中央高校基本科研业务费专项资金资助项目(17CX02054);研究生创新工程资助项目(YCX2017058)

Multiblock local Fisher discriminant analysis for chemical process fault classification

WANG Lei, DENG Xiaogang^*, CAO Yuping, TIAN Xuemin

College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, Shandong, China

Received:2017-04-18 Online:2017-10-20 Published:2017-04-18

摘要/Abstract

摘要： Fisher判别分析(FDA)是一种有效的化工过程故障模式分类方法,但是其忽视了数据局部结构信息的挖掘。针对该问题,提出一种多块局部Fisher判别分析(MLFDA)方法,以更有效地识别化工过程故障。从变量和样本两个维度来分析数据的局部结构特性。针对变量维度的局部信息挖掘问题,设计了一种基于变量与数据集主元空间的相关度的变量分块方法,将全局过程变量划分为多个局部变量块。进一步考虑到样本维度的局部结构特性,应用基于局部权重因子的局部Fisher判别分析(LFDA)为每个局部变量块构建分类器。提出一种基于分类性能加权的多分类器集成方法,以融合不同分类器的决策结果。在Tennessee Eastman过程上的仿真结果说明,MLFDA方法具有比传统的FDA和LFDA方法更低的故障误分类率。

关键词: Fisher判别分析, 故障模式分类, Tennessee Eastman过程, 局部Fisher判别分析, 多块局部Fisher判别分析

Abstract: Fisher discriminant analysis(FDA)was an effective chemical process fault classification method. However, the local data structure information was not investigated within traditional FDA method. To deal with this problem, a multiblock local Fisher discriminant analysis(MLFDA)method was proposed for more effective chemical process fault recognition. This method analyzed the local data structure characteristics from the variable-dimension and sample-dimension. To mine the local information in the variable-dimension, a variable block division method was designed based on the relevancy between the variables and the principal component subspace of the dataset, with which all the variables could be divided into several local variable blocks. Furthermore, considering the characteristics of local sample structure, the local FDA(LFDA)using local weighting factors was applied to construct classifier for each local variable block. An integrating strategy based on weighting classification performance weighting was presented to combine the results from different classifiers. Simulation results on Tennessee Eastman process showed that the proposed MLFDA method had a lower misclassification rate than traditional FDA and LFDA methods.

Key words: local Fisher discriminant analysis, multiblock local Fisher discriminant analysis, fault classification, Fisher discriminant analysis, Tennessee Eastman process

中图分类号:

TP277

王磊,邓晓刚,曹玉苹,田学民. 基于MLFDA的化工过程故障模式分类方法[J]. 山东大学学报(工学版), 2017, 47(5): 179-186.

WANG Lei, DENG Xiaogang, CAO Yuping, TIAN Xuemin. Multiblock local Fisher discriminant analysis for chemical process fault classification[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 179-186.

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基于MLFDA的化工过程故障模式分类方法

Multiblock local Fisher discriminant analysis for chemical process fault classification

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