Fault detection and diagnosis using an art-based neural network

Keem Siah Yap, Mau Teng Au, Chee Peng Lim, Junita Mohamad Saleh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Generalized Adaptive Resonance Theory (GART) network is a neural network model based on the integration of Gaussian ARTMAP and the Generalized Regression Neural Network. It is capable of online learning, and is effective in tackling classification as well as regression tasks, as demonstrated in our previous work. In this paper, we further enhance the capability of the GART network with the Laplacian functions and with new vigilance and match-tracking mechanisms. In addition, a rule extraction procedure is incorporated into its dynamics, and its applicability to fault detection and diagnosis tasks is assessed. IF-THEN rules can be extracted from the weights of the trained GART network after a pruning process. The classification and rule extraction capability of GART are evaluated using one benchmark data set from medical application, and one real data set collected from a power generation plant. These results are then compared with those reported by other methods. The outcomes demonstrate that GART is able to produce high classification rates with quality rules for tackling fault detection and diagnosis problems.

Original languageEnglish
Title of host publicationProceedings of the 10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010
PublisherACTA Press
Pages118-125
Number of pages8
ISBN (Print)9780889868182
DOIs
Publication statusPublished - 01 Jan 2010
Event10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010 - Innsbruck, Austria
Duration: 15 Feb 201017 Feb 2010

Publication series

NameProceedings of the 10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010

Other

Other10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010
CountryAustria
CityInnsbruck
Period15/02/1017/02/10

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

Cite this

Yap, K. S., Au, M. T., Lim, C. P., & Saleh, J. M. (2010). Fault detection and diagnosis using an art-based neural network. In Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010 (pp. 118-125). (Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010). ACTA Press. https://doi.org/10.2316/p.2010.674-102