Application of a neural fuzzy system with rule extraction to fault detection and diagnosis

  • Chen K
  • Lim C
  • Lai W
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Abstract

In this paper, the fuzzy min–max (FMM) neural network is integrated
with a rule extraction

algorithm, and the resulting network is applied to a real-world fault
detection and

diagnosis task in complex industrial processes. With the rule extraction
capability, the

FMM network is able to overcome the “black-box��? phenomenon by
justifying its predictions

using fuzzy if–then rules that are comprehensible to domain users.
To assess the

effectiveness of the FMM network, real sensor measurements are collected
and used for

detecting and diagnosing the heat transfer and tube blockage conditions
of a circulating

water (CW) system in a power generation plant. The FMM network parameters
are systematically

varied and tested, with the results explained. Bootstrapping is employed
to

quantify stability of the network performance statistically. The extracted
rules are found

to be compatible with the domain information as well as the opinions
of domain experts

who are involved in the maintenance of the CW system. Implications
of the FMM network

with the rule extraction facility as an intelligent and useful fault
detection and diagnosis

tool are discussed.

Author-supplied keywords

  • Fault detection and diagnosis
  • fuzzy min–max neural network
  • rule extraction

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Authors

  • Kok Yeng Chen

  • Chee Peng Lim

  • Weng Kim Lai

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