Human-like fault diagnosis using a neural network implementation of plausibility and relevance

Viorel Ariton, Vasile Palade

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In real systems, fault diagnosis is performed by a human diagnostician, and it encounters complex knowledge associations, both for normal and faulty behaviour of the target system. The human diagnostician relies on deep knowledge about the structure and the behaviour of the system, along with shallow knowledge on fault-to-manifestation patterns acquired from practice. This paper proposes a general approach to embed deep and shallow knowledge in neural network models for fault diagnosis by abduction, using neural sites for logical aggregation of manifestations and faults. All types of abduction problems were considered. The abduction proceeds by plausibility and relevance criteria multiply applied. The neural network implements plausibility by feed-forward links between manifestations and faults, and relevance by competition links between faults. Abduction by plausibility and relevance is also used for decision on the next best test along the diagnostic refinement. A case study on an installation in a rolling mill plant is presented.

Original languageEnglish
Pages (from-to)149-165
Number of pages17
JournalNeural Computing and Applications
Volume14
Issue number2
Early online date24 Nov 2004
DOIs
Publication statusPublished - Jul 2005
Externally publishedYes

Keywords

  • Abduction problem
  • Fault diagnosis
  • Fuzzy logic
  • Incremental diagnosis
  • Neural networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence

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