A Modified Adaptive Chaotic Binary Ant System and Its Application in Chemical Process Fault Diagnosis

  • Ling Wang
  • Jinshou Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


Fault diagnosis is a small sample problem as fault data are absent in the real production process. To tackle it, Support Vector Machines (SVM) is adopted to diagnose the chemical process steady faults in this paper. Considering the high data dimensionality in the large-scaled chemical industry seriously spoil classification capability of SVM, a modified adaptive chaotic binary ant system (ACBAS) is proposed and combined with SVM for fault feature selection to remove the irrelevant variables and ensure SVM classifying correctly. Simulation results and comparisons of Tennessee Eastman Process show the developed ACBAS can find the essential fault feature variables effectively and exactly, and the SVM fault diagnosis method combined with ACBAS-based feature selection greatly improve the diagnosing performance as unnecessary variables are eliminated properly.


Support Vector Machine Feature Selection Fault Diagnosis Feature Selection Method Chaotic Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ling Wang
    • 1
  • Jinshou Yu
    • 1
  1. 1.Research Institution of AutomationEast China University of Science & TechnologyShanghaiChina

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