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

100%
Kościelny J. M., Syfert M.
International Journal of Applied Mathematics and Computer Science
|
2006
|
tom 16
|
nr 1
27-35
EN
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.
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Fault isolability with different forms of the faults-symptoms relation

81%
Kościelny J., Syfert M., Rostek K., Sztyber A.
International Journal of Applied Mathematics and Computer Science
|
2016
|
tom 26
|
nr 4
815-826
EN
The definitions and conditions for fault isolability of single faults for various forms of the diagnostic relation are reviewed. Fault isolability and unisolability on the basis of a binary diagnostic matrix are analyzed. Definitions for conditional and unconditional isolability and unisolability on the basis of a fault information system (FIS), symptom sequences and directional residuals are formulated. General definitions for conditional and unconditional isolability and unisolability in the cases of simultaneous evaluation of diagnostic signal values and a sequence of symptoms are provided. A comprehensive example is discussed.
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