基于集成局部费舍尔判别分析的故障分类  

Integrated local Fisher discriminant analysis based fault classification

作　　者：钟凯 徐明星 韩敏[2] ZHONG Kai;XU Ming-xing;HAN Min(Faculty of Electronic Information and Electrical Engineering,Dalian University of Technology,Dalian Liaoning 116024,China;Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education,Dalian University of Technology,Dalian Liaoning 116024,China;Institutes of Physical Science and Information Technology,Anhui University,Hefei Anhui 230601,China)

机构地区：[1]大连理工大学电子信息与电气工程学部,辽宁大连116024 [2]大连理工大学工业装备智能控制与优化教育部重点实验室,辽宁大连116024 [3]安徽大学物质科学与信息技术研究院,安徽合肥230601

出　　处：《控制理论与应用》2021年第4期489-495,共7页Control Theory & Applications

基　　金：中央高校基本科研业务费项目(DUT20LAB114)资助.

摘　　要：实际工业过程数据的局部特性一般都较为复杂,不利于样本特征的提取和故障分类精度的提高.针对此问题,本文提出一种集成的局部费舍尔判别分析(ILFDA)模型,可以同时从变量和样本两个维度挖掘数据的局部结构特征,提高故障分类的性能并降低建模的难度.首先,根据过程的结构原理对复杂系统进行分块,从而可以有效获取变量维度的数据局部信息,并排除无关变量的影响.其次,针对样本维度的数据局部信息,在每个变量子块中分别建立局部费舍尔判别分析(LFDA)模型,并为每个局部模型分配相应的权值,从而可以更准确地衡量不同子块对当前故障的影响程度.最后,利用分类性能加权策略将各个子块的分类结果进行融合.田纳西–伊斯曼(TE)过程中的仿真结果验证本文所提的ILFDA方法具有更好的故障分类效果.The actual industrial process data is often companied with complex local characteristics,which is not conducive to the extraction of sample features and the improvement of fault classification accuracy.To solve this problem,an integrated local Fisher discriminant analysis(ILFDA)model is proposed in this paper,which can mine the local structure characteristics of data from variable and sample dimensions simultaneously,thus fault classification accuracy is improved and the difficulty of modeling is reduced.Firstly,the complex system is partitioned based on the structure principle,so that the local information of data can be obtained from the variable dimension efficiently and the influence of irrelevant variables is excluded.Secondly,as for the local information from sample dimension,local Fisher discriminating analysis(LFDA)classification model is established in each sub-block,and corresponding weights are assigned to local models,so as to measure the influence of different sub-blocks on current fault more accurately.Finally,the classification performance weighting strategy is used to fuse the classification results in each sub-block.The simulation results on Tennessee Eastman(TE)process show that the proposed ILFDA method has better fault classification performance.

关 键 词：故障分类 局部费舍尔判别分析 分类结果集成 数据局部结构特征 

分 类 号：TH17[机械工程—机械制造及自动化]