引用本文:钟凯,徐明星,韩敏.基于集成局部费舍尔判别分析的故障分类[J].控制理论与应用,2021,38(4):489~495.[点击复制]
ZHONG Kai,XU Ming-xing,HAN Min.Integrated local Fisher discriminant analysis based fault classification[J].Control Theory and Technology,2021,38(4):489~495.[点击复制]
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
摘要点击 2203  全文点击 629  投稿时间：2020-06-01  修订日期：2020-10-11
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DOI编号  10.7641/CTA.2020.00316
  2021,38(4):489-495
中文关键词  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
英文关键词  fault classification  local Fisher discriminant analysis  classification results integration  local characteristics of data
基金项目  中央高校基本科研业务费项目(DUT20LAB114)资助.
作者单位E-mail
钟凯 大连理工大学 zhongkai0402@mail.dlut.edu.cn 
徐明星 大连理工大学  
韩敏* 大连理工大学 minhan@dlut.edu.cn 
中文摘要
      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.
英文摘要
      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.