Fuzzy diagnostic reasoning that takes into account the uncertainty of the relation between faults and symptoms

Jan Kościelny; Michał Syfert

International Journal of Applied Mathematics and Computer Science (2006)

  • Volume: 16, Issue: 1, page 27-35
  • ISSN: 1641-876X

Abstract

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Knowledge about the relation between faults and the observed symptoms is necessary for fault isolation. Such a relation can be expressed in various forms, including binary diagnostic matrices or information systems. The paper presents the use of fuzzy logic for diagnostic reasoning. This method enables us to take into account various kinds of uncertainties connected with diagnostic reasoning, including the uncertainty of the faults-symptoms relation. The presented methods allow us to determine the fault certainty factor as well as certainty factors of the normal and unknown process states. The unknown process state factor groups all the states with unknown and multiple faults with the states with improper residual values, while the normal state factor indicates similarity between the observed state and the pattern fault-free state.

How to cite

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Kościelny, Jan, and Syfert, Michał. "Fuzzy diagnostic reasoning that takes into account the uncertainty of the relation between faults and symptoms." International Journal of Applied Mathematics and Computer Science 16.1 (2006): 27-35. <http://eudml.org/doc/207775>.

@article{Kościelny2006,
abstract = {Knowledge about the relation between faults and the observed symptoms is necessary for fault isolation. Such a relation can be expressed in various forms, including binary diagnostic matrices or information systems. The paper presents the use of fuzzy logic for diagnostic reasoning. This method enables us to take into account various kinds of uncertainties connected with diagnostic reasoning, including the uncertainty of the faults-symptoms relation. The presented methods allow us to determine the fault certainty factor as well as certainty factors of the normal and unknown process states. The unknown process state factor groups all the states with unknown and multiple faults with the states with improper residual values, while the normal state factor indicates similarity between the observed state and the pattern fault-free state.},
author = {Kościelny, Jan, Syfert, Michał},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {uncertainty; diagnostic relation; fuzzy logic; fault isolation},
language = {eng},
number = {1},
pages = {27-35},
title = {Fuzzy diagnostic reasoning that takes into account the uncertainty of the relation between faults and symptoms},
url = {http://eudml.org/doc/207775},
volume = {16},
year = {2006},
}

TY - JOUR
AU - Kościelny, Jan
AU - Syfert, Michał
TI - Fuzzy diagnostic reasoning that takes into account the uncertainty of the relation between faults and symptoms
JO - International Journal of Applied Mathematics and Computer Science
PY - 2006
VL - 16
IS - 1
SP - 27
EP - 35
AB - Knowledge about the relation between faults and the observed symptoms is necessary for fault isolation. Such a relation can be expressed in various forms, including binary diagnostic matrices or information systems. The paper presents the use of fuzzy logic for diagnostic reasoning. This method enables us to take into account various kinds of uncertainties connected with diagnostic reasoning, including the uncertainty of the faults-symptoms relation. The presented methods allow us to determine the fault certainty factor as well as certainty factors of the normal and unknown process states. The unknown process state factor groups all the states with unknown and multiple faults with the states with improper residual values, while the normal state factor indicates similarity between the observed state and the pattern fault-free state.
LA - eng
KW - uncertainty; diagnostic relation; fuzzy logic; fault isolation
UR - http://eudml.org/doc/207775
ER -

References

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  1. Combastel C., Gentil S. and Rognon J.P. (2003): Toward a better integration of residual generation and diagnostic decision. - Proc. 5-th IFAC Symp. Fault Detection, Supervision and Safety for Technical Process, SAFEPROCESS'2003, Washington, USA, CD-ROM. 
  2. Frank P.M. (1994): Fuzzy supervision. Application of fuzzy logic to process supervision and fault diagnosis. - Proc. Workshop Fuzzy Technologies in Automation and Intelligent Systems, Duisburg, Germany, pp. 36-59. 
  3. Frank P.M. and Marcu T. (2000): Diagnosis strategies and system: principle, fuzzy and neural approaches, In: Intelligent Systems and Interfaces (H.N. Teodorescu, D. Mlynek, A. Kandel and H.-J. Zimmermann, Eds.). -The Kluwer International Series in Intelligent - International Series in Intelligent Technologies, Springer, Chap. 11. 
  4. Garcia F.J., Izquierdo V., de Miguel L., and Peran J. (1997): Fuzzy Identification of Systems and its Applications to Fault Diagnosis Systems. - Proc. IFAC Symp. Fault Detection Supervision and Safety for Technical Processes, SAFEPROCESS'97, Kingston Upon Hull, UK, Vol. 2., pp. 705-712. 
  5. Korbicz J., Kościelny J.M., Kowalczuk Z. and Cholewa W. (2004): Fault Diagnosis. Models. Artificial intelligence. Applications. - Berlin: Springer. Zbl1074.93004
  6. Kościelny J.M. (1999): Application of fuzzy logic fault isolation in a three-tank system. - Proc. 14-th IFAC World Congress, Bejing, China, pp. 73-78. 
  7. Kościelny J.M. (2001): Diagnostics of Automated Industrial Processes.- Warsaw: Exit, (in Polish). 
  8. Kościelny J.M., Sędziak D. and Zakroczymski K. (1999): Fuzzy logic fault isolation in large scale systems. - Int. J. Appl. Math. Comput. Sci., Vol. 9, No. 3, pp. 637-652. Zbl0945.93509
  9. Kościelny J.M. and Syfert M. (2000): Current diagnostics of power boiler system with use of fuzzy logic. - Proc. 4-th IFAC Symp. Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS'2000, Budapest, Hungary, Vol. 2, pp. 681-686. 
  10. Kościelny J.M. and Syfert M. (2004): Fuzzy logic application for the description of diagnostic relation uncertainty. - Proc. 10-th IEEE Int. Conf. Methods and Models in Automation and Robotics, Międzyzdroje, Poland, pp. 757-762. 
  11. Pawlak Z. (1983): Information Systems. Theoretical Background. -Warsaw: WNT, (in Polish). 
  12. Piegat A. (2001): Fuzzy Modelling and Control. - Berlin: Springer. Zbl0976.93001
  13. Sędziak D. (2001): Methods of fault isolation in industrial processes. - Ph.D. thesis, Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland, (in Polish). 
  14. Syfert M. (2003): The diagnostics of industrial processes with the use of partial models and fuzzy logic. - Ph.D. thesis, Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland, (in Polish). 

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