JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (5): 179-186.doi: 10.6040/j.issn.1672-3961.0.2017.181

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Multiblock local Fisher discriminant analysis for chemical process fault classification

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

  1. 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

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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

CLC Number: 

  • TP277
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