JACIII Vol.15 No.3 pp. 336-344
doi: 10.20965/jaciii.2011.p0336


Fuzzy Nonlinear Regression Analysis Using Fuzzified Neural Networks for Fault Diagnosis of Chemical Plants

Daisaku Kimura*,**, Manabu Nii*, Takafumi Yamaguchi*,
Yutaka Takahashi*, and Takayuki Yumoto*

*Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2201, Japan

**Corporate Technology Administration Department, KANEKA Corporation, 3-2-4 Nakanoshima, Kita-ku, Osaka, Japan

November 5, 2010
December 21, 2010
May 20, 2011
fault diagnosis, plant operation, fuzzified neural networks, fuzzy nonlinear regression
In systems such as chemical plants or circulatory systems, failure of piping, sensors or valves causes serious problems. These failures can be avoided by the increase in sensors and operators for condition monitoring. However, since adding sensors and operators leads to an increase in cost, it is difficult to realize. In this paper, a technique of diagnosing target systems based on a fuzzy nonlinear regression is proposed by using a fuzzified neural network that is trained with time-series data with reliability grades. Our proposed technique uses numerical data recorded by the existing monitoring system. Reliability grades are beforehand given to the recorded data by domain experts. The state of a target system is determined based on the fuzzy output from the trained fuzzified neural network. Our proposed technique makes us determine easily the state of the target systems. Our proposed technique is flexibly applicable to various types of systems by considering some parameters for failure determination of target systems.
Cite this article as:
D. Kimura, M. Nii, T. Yamaguchi, Y. Takahashi, and T. Yumoto, “Fuzzy Nonlinear Regression Analysis Using Fuzzified Neural Networks for Fault Diagnosis of Chemical Plants,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.3, pp. 336-344, 2011.
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