Fuzzy-based refinement of the fault diagnosis task in industrial devices

C. D. Bocaniala, J. Sa Da Costa, V. Palade

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


This paper first describes a fuzzy classifier to be used for fault diagnosis. Then, the paper presents a refinement of the diagnosis task performed with this fuzzy classifier. For each fault, a number of 20 levels of fault strength have been considered. In previous work, more than one single category per fault has been used to improve the classifier performance, i.e. distributing the strength levels into small, medium and, respectively large strength subsets. However, this distribution scheme is too rigid. This paper introduces a flexible distribution scheme that takes into account the (di)similarities between different strength levels. The refinement proposed here offers better insight on the behavior of each fault and it increases separation between overlapping faults, which improves the final outcome of the diagnosis process.

Original languageEnglish
Pages (from-to)599-614
Number of pages16
JournalJournal of Intelligent Manufacturing
Issue number6
Publication statusPublished - Dec 2005
Externally publishedYes


  • Fault diagnosis
  • Fault isolation refinement
  • Fuzzy logic
  • Particle swarm optimisation
  • Pattern recognition

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence


Dive into the research topics of 'Fuzzy-based refinement of the fault diagnosis task in industrial devices'. Together they form a unique fingerprint.

Cite this