Diagnostics of faulty states in complex physical systems using fuzzy relational equations

ABSTRACT The paper presents a method for diagnostics of physical system failures based on the fuzzy relational equation with the max-mm composition. The heuristic algorithm for inverse problem solving is briefly described. Since proper description of the plant properties is significant to increase efficiency of the diagnostics two versions of the relationship matrix extension are proposed. The first one considers different directions of failure and symptom deviations from their nominal values. The second one deals with on-line extensions of the relationship matrix in a case of unexpected failure occurrence. Finally the diagnostic system with some elements of fuzzy logic is discussed in order to improve man-machine communications. For test purposes the systems have been applied for failure analysis of the THTR-300 nuclear power plant modelled by a computer code.

[1]  D. J. Wells,et al.  Applications possibilities for fuzzy failure analysis, and diagnosis of reactor plant components and areas , 1980 .

[2]  J. Mościński,et al.  A fuzzy-logic approach to HTR nuclear power plant model control , 1983 .

[3]  Tsutomu Hoshino,et al.  In-Core Fuel Management Optimization by Heuristic Learning Technique , 1972 .

[4]  A heuristic approach to the reinforcement-learning control of the one-dimensional model of an HTR core , 1982 .

[5]  H. R. van Nauta Lemke,et al.  Application of a fuzzy controller in a warm water plant , 1976, Autom..

[6]  Jiro Wakabayashi,et al.  Simulation Study of a System for Diagnosis of Nuclear Power Plant Operation , 1981 .

[7]  Jacek Kitowski,et al.  Fuzzy logic applications for failure analysis and diagnosis of a primary circuit of the HTR nuclear power plant , 1985 .

[8]  T. Terano,et al.  Failure diagnosis by using fuzzy logic , 1977, 1977 IEEE Conference on Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications.

[9]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[10]  George J. Klir,et al.  Identification of fuzzy relation systems , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  David D. Ebert Practicality of and benefits from the applications of optimal control to pressurized water reactor maneuvers , 1982 .

[12]  H. Zimmermann DESCRIPTION AND OPTIMIZATION OF FUZZY SYSTEMS , 1975 .

[13]  Billy V. Koen,et al.  Application of artificial intelligence techniques to digital computer control of nuclear reactors , 1975 .

[14]  W. B. Terney,et al.  Optimal Control of Nuclear Reactor Depletion , 1970 .

[15]  Witold Pedrycz Identification in fuzzy systems , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[16]  J. Mościński,et al.  Computer simulation of heuristic reinforcement-learning systems for nuclear power plant load changes control , 1979 .

[17]  J. F. Baldwin,et al.  Fuzzy logic and approximate reasoning for mixed input arguments , 1979 .

[18]  G. Klir,et al.  Resolution of finite fuzzy relation equations , 1984 .

[19]  Ritsuo Oguma Extended Partial and Multiple Coherence Analyses and Their Application to Reactor Noise Investigation , 1982 .

[20]  A. Mogilner,et al.  On the Problem of Noise Spectra Classification in Nuclear Power Plant Operation Diagnostics , 1981 .

[21]  E. Sanchez,et al.  Inverses of fuzzy relations: Application to possibility distributions and medical diagnosis , 1977, 1977 IEEE Conference on Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications.

[22]  Elie Sanchez,et al.  Resolution of Composite Fuzzy Relation Equations , 1976, Inf. Control..