single-jc.php

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

Paper:

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

Received:
November 5, 2010
Accepted:
December 21, 2010
Published:
May 20, 2011
Keywords:
fault diagnosis, plant operation, fuzzified neural networks, fuzzy nonlinear regression
Abstract

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:
Daisaku Kimura, Manabu Nii, Takafumi Yamaguchi,
Yutaka Takahashi, and Takayuki 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.
Data files:
References
  1. [1] K. Nabeshima, “A study of Reactor Monitoring Method with Neural Network,” JAERI1342, Japan Atomic Energy Research Institute, 2001. (in Japanese)
  2. [2] H.Maruta, M. Kano, H. Kugemoto, and K. Shimizu, “Modeling and detection of stiction of control valve,” Japan Society for the Promotion of Science, No.143 committee of Process System Engineering, Workshop, No.25 final report, 2005. (in Japanese)
  3. [3] J. Eino, T. Wakui, T. Hashizume, N. Miyachi, Y. Saito, K. Kuromori, and Y. Yuki, “Diagnosis of Impulse Line Blockage with Differential Pressure Transmitter: Part3: Diagnosis under transient states,” The Japan Society of Mechanical Engineers, Dynamics, Measurement and Control Division, Dynamics and Design Conference (CD-ROM), Vol.2006, No.314, 2006. (in Japanese)
  4. [4] M. Fukuda, “Toward Optimization/High Promotion of Efficiency of Control Valve Maintenance,” Japan TAPPI Journal, Vol.60, No.3, 2006. (in Japanese)
  5. [5] J. Zhang and A. J. Morris, “On-line process fault diagnosis using fuzzy neural networks,” Intelligent Systems Engineering, Vol.3, No.1, pp. 37-47, 1994.
  6. [6] J. Zhang and J. Morris, “Process modelling and fault diagnosis using fuzzy neural networks,” Fuzzy Sets and Systems, Vol.79, pp. 127-140, 1996.
  7. [7] H. Wang and W. W. Keerthipala, “Fuzzy-Neuro Approach to Fault Classification for Transmission Line Protection,” IEEE Trans. on Power Delivery, Vol.13, No.4, pp. 1093-1104, 1998.
  8. [8] R. Javadpour and G. M. Knapp, “A fuzzy neural network approach to machine condition monitoring,” Computers & Industrial Engineering, Vol.45, pp. 323-330, 2003.
  9. [9] S. Zhang, T. Asakura, X. Xu, and B. Xu, “Fault Diagnosis System for Rotary Machine Based on Fuzzy Neural Networks,” JSME Int. J., Series C, Vol.46, No.3, pp. 1035-1041, 2003.
  10. [10] H. Ishibuchi and M. Nii, “Fuzzy Regression using Asymmetric Fuzzy Coefficients and Fuzzified Neural Networks,” Fuzzy Sets and Systems, Vol.119, pp. 273-290, 2001.
  11. [11] H. Ishibuchi and M. Nii, “Numerical Analysis of the Learning of Fuzzified Neural Networks from Fuzzy If-Then Rules,” Fuzzy Sets and Systems, Vol.130, pp. 281-307, 2001.
  12. [12] D. Kimura, M. Nii, Y. Takahashi, and T. Yumoto, “Development of a Fault Diagnosis System based on Fuzzified Neural Networks,” Proc. of FUZZ-IEEE 2010, pp. 1169-1174, 2010.
  13. [13] D. Kimura, M. Nii, T. Yamaguchi, Y. Takahashi, and T. Yumoto, “Fuzzy Nonlinear Regression Analysis using Fuzzified Neural Networks for Fault Diagnosis,” Proc. of The 4th Int. Symposium on Computational Intelligence and Industrial Applications, pp. 35-40, 2010.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 01, 2021