Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Development of Neuro-Fuzzy-Based Fault Diagnostic System for Closed-Loop Control system

페푸프 제어 시스템을 위한 퍼지-신경망 기방 고장 진단 시스템의 개발

  • Kim, Seong-Ho (Dept.of Electronics Information Engineering, Kunsan National University) ;
  • Lee, Seong-Ryong (Dept.of Electronics Information Engineering, Kunsan National University) ;
  • Gang, Jeong-Gyu (Dept.of Electronics Information Engineering, Kunsan National University)
  • 김성호 (군산대학교 전자정보공학부) ;
  • 이성룡 (군산대학교 전자정보공학부) ;
  • 강정규 (군산대학교 전자정보공학부)
  • Published : 2001.06.01
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Abstract

In this paper an ANFIS(Adativo Neuro-Fuzzy Inference System)- based fault detection and diagnosis for a closed loop control system is proposed. The proposed diagnostic system contains two ANFIS. One is run as a parallel model within the model in closed loop control(MCL) and the other is run as a series-parallel model within the process in closed loop(PCL) for the generation of relevant symptoms for fault diagnosis. These symptoms are further processed by another classification logic with simple rules and neural network for process and controller fault diagnosis. Experimental results for a DC shunt motor control system illustrate the effectiveness of the proposed diagnostic scheme.

Keywords

References

  1. R., Isermann, 'Integration of fault detection and diagnosis methods', IFAC-Symposium SAFEPROESS'94, Espoo, pp. 597-612, 1994
  2. A. S., Willsky, 'A survey of design methods for failure detection in dynamic systems', Automatica, vol. 12, pp. 601-611, 1976 https://doi.org/10.1016/0005-1098(76)90041-8
  3. R., Isermman, 'On the application of model-based fault detection for technical processes', Control Engineering Practice, vol. 2, no. 3, pp. 439-450, 1994 https://doi.org/10.1016/0967-0661(94)90781-1
  4. R. Patton and P. Frank, Fault diagnosis in dynamic systems. Theory and application, Prentice Hall Int. UK
  5. T. Sorsa and H., Koivo, 'Dynamic fault diagnosis using radial basis function networks', TOOLDIAG'93, Toulouse, I/160-169, 1993
  6. R. J. Patton, and J, Chen, 'Fault diagnosis in nonlinear dynamic systems via neural networks', Proc. IEE Int. Conf. Control'94, vol. 2, pp. 1346-1351, 1994 https://doi.org/10.1049/cp:19940332
  7. D., Fussel, P., Balle, R. and Isermman, 'Closed loop fault diagnosis based on a nonlinear process model and automatic fuzzy rule generation', IFAC symp. on Safeprocess'97, vol. 1, pp. 359-364, 1997
  8. T. Sorsa, 'Neural networks in process fault diagnosis', IEEE Trans. on Syst, Man and Cybernetics, vol. 21, no. 4, pp. 815-825, 1991 https://doi.org/10.1109/21.108299
  9. J. S. Jang, 'ANFIS: Adaptive-network-based fuzzy inference systems', IEEE Trans. on Systems, Man, and Cybernetics, vol. 23, pp. 665-685, May, 1993 https://doi.org/10.1109/21.256541
  10. 김성호, 김정수, 박귀태, 박태홍, 이종렬, '퍼지-신경망기반 고장진단 시스템의 설계', 대한전기학회, vol. 48A, no. 10, 1999