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DOI: 10.15507/2658-4123.031.202103.364-379


Knowledge Modeling in Troubleshooting


Valeriy P. Dimitrov
Head of the Chair of Quality Management, Don State Technical University (1 Gagarin Sq., Rostov-on-Don 344000, Russian Federation), D.Sc. (Engr.), Professor, Researcher ID: E-4908-2018, ORCID: https://orcid.org/0000-0003-1439-1674, Scopus ID: 57195505958, This email address is being protected from spambots. You need JavaScript enabled to view it.

Lyudmila V. Borisova
Head of the Chair of Management and Business Processes, Don State Technical University (1 Gagarin Sq., Rostov-on-Don 344000, Russian Federation), D.Sc. (Engr.), Professor, Researcher ID: E-4863-2018, ORCID: https://orcid.org/0000-0001-6611-4594, This email address is being protected from spambots. You need JavaScript enabled to view it.

Kaprel L. Hubiyan
Associate Professor of the Chair of Quality Management, Don State Technical University (1 Gagarin Sq., Rostov-on-Don 344000, Russian Federation), Cand.Sc. (Engr.), ORCID: https://orcid.org/0000-0001-8743-6672, This email address is being protected from spambots. You need JavaScript enabled to view it.

Introduction. The article describes the approach to solving the problem of complex technical system troubleshooting based on expert knowledge modeling. Intelligent information systems are widely used to solve the problems of diagnostics of multilevel systems including combine harvesters. The formal description of the subject domain knowledge is the framework for building the knowledge base of these systems. The sequence of creating an expert system knowledge base in accordance with production rules is considered.
Materials and Methods. The approach is founded on the fault function table. As the object of diagnostics, one of the subsystems of the combine harvester electric equipment “opening the hopper roof flaps” is considered. The basis for constructing a sequence of elementary checks is a system of logical equations describing both the serviceable and possible faulty states of the subsystem.
Results. A structural logic model is developed. As a result of analyzing the fault function table, the sets of elementary checks are determined. Four criteria have been used to analyze the weight of these checks. The authors have determined optimal sequence of checks and have developed a decision tree, which allows finding the cause of the malfunction and is the basis for creating the knowledge base of an intelligent information system. A fragment of the knowledge base is given.
Discussion and Conclusion. The proposed approach of expert knowledge modelling increases the efficiency of the unit for troubleshooting of the intelligent decision support system. It makes possible to structure the base of expertise and establishing the optimal sequence of elementary checks. This allows determining the optimal sequence of application of the knowledge base production rule that makes it possible to reduce the time of restoring the serviceability of combines.

Keywords: intelligent information system, decision making, combine harvester, fault diagnostics, production rules, knowledge base

Conflict of interest: The authors declare no conflict of interest.

For citation: Dimitrov V.P., Borisova L.V., Hubiyan K.L. Knowledge Modeling in Troubleshooting. Inzhenernyye tekhnologii i sistemy = Engineering Technologies and Systems. 2021; 31(3):364-379. DOI: https://doi.org/10.15507/2658-4123.031.202103.364-379

Contribution of the authors:
V. P. Dimitrov – subject area analysis, knowledge modeling.
L. V. Borisova – development of the knowledge formalization scheme and the structural-logical scheme of the electrical equipment subsystem.
K. L. Hubiyan – developing a table of fault functions, constructing a decision tree, obtaining the results of an illustrative example, formulating knowledge base rules.

All authors have read and approved the final manuscript.

Received 15.03.2021; approved after reviewing 25.04.2021;
accepted for publication 14.05.2021



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