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A Fuzzy Classification Technique Applied to Fault Diagnosis

  • Cosmin Danut Bocaniala
  • José Sá da Costa
Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

This chapter describes a novel fuzzy classification methodology for fault diagnosis. There are three main directions of applying fuzzy classifiers to fault diagnosis: neuro-fuzzy classifiers, classifiers based on collections of fuzzy rules, and classifiers based on collections of fuzzy subsets. The contributed fuzzy classification methodology described in this chapter follows the last direction. The main advantages of the developed fuzzy classifier are the high accuracy with which it delimits the areas corresponding to different system states, i.e., the normal state and the different faulty states, and the fine precision of discrimination inside overlapping areas. In addition, the classifier needs to tune only a small numbers of parameters, i.e., the number of parameters equals the number of system states considered. The methodology is validated by application with very good results to fault diagnosis of a control flow valve from an industrial device.

Keywords

Similarity Measure Fuzzy Rule Fault Diagnosis Fuzzy Subset Fuzzy Membership Function 
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 London Limited 2006

Authors and Affiliations

  • Cosmin Danut Bocaniala
    • 1
  • José Sá da Costa
    • 2
  1. 1.Computer Science and Engineering Department“Dunarea de Jos” University of GalatiGalatiRomania
  2. 2.Department of Mechanical Engineering, GCAR/IDMECTechnical University of LisbonLisbonPortugal

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