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Verification of Mixed Production Rules Correctness in Intelligent Systems for Diagnosing Industrial Equipment

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SMART Automatics and Energy

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 272))

Abstract

In this paper, we propose an approach to verifying the correctness of mixed production rules (MPR) in intelligent systems for diagnosing industrial equipment (IE) using the rule evaluation that is necessary to determine the degree of similarity between two rules. Complexity, relevance, and significance of the rules serve as the rule evaluation. Using rule evaluations, it is easy to detect incorrect and redundant rules in the knowledge base (KB) of IE diagnostic systems under conditions of heterogeneous information. The examples of main structural errors that occur in MPR systems (redundancy, cyclicity, incompleteness) are considered in details. The approach proposed is firstly aimed at simplifying the KB containing MPR that are added to the intelligent system of IE diagnostics which results in usefulness and accessibility for the knowledge interpretation by an expert; secondly, it will allow structuring the fuzzy knowledge and facilitating the organization of output control.

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References

  1. Nguen, T., Perkins, W., Laffey, T., Pecora, W.: Checking expert system knowledge bases for consistency and completeness. In: Proceedings of the 9th International Joint Conference on Artificial Intelligence (Los Angeles), pp. 375–78 (1985)

    Google Scholar 

  2. Dolinina, O., Suchkova, N.: Formal models of the structural errors in the knowledge bases of intellectual decision making systems. In: Computer Science On-line Conference, pp. 156–67 (2017)

    Google Scholar 

  3. Ternovoi, M.Y.: Formal specification of properties of Mamdani fuzzy knowledge bases based on metagraph. Bullet. V.N. Karazin Kharkiv National Univ. 27, 157–71 (2015)

    Google Scholar 

  4. Globa, L., Savchuk, Z., Vasylenko, O., Siemens, E.: QOS of data networks analyzing based on the fuzzy knowledge base. In: International Conference Info Communications-Present and Future, pp. 130–49 (2020)

    Google Scholar 

  5. Cao, Q., Zanni-Merk, C., Samet, A., de Beuvron, F.D.B., Reich, C.: Using rule quality measures for rule base refinement in knowledge-based predictive maintenance systems. Cybern. Syst. 51(2), 161–176 (2020)

    Article  Google Scholar 

  6. Mitra, S., Anand, K., Chittimalli, P.K.: Identifying anomalies in sbvr-based business rules using directed graphs and SMT-LIBv2. In: ICEIS 2, pp. 215–22 (2018)

    Google Scholar 

  7. Pegat, A.: Fuzzy Modeling and Control. Laboratory of Knowledge, Moscow: BINOM, p. 800 (2017)

    Google Scholar 

  8. Kolodenkova, A.E., Vereshchagina, S.S., Vereshchagin, V.E.: Integrated approach to processing diagnostic data based on heterogeneous cognitive models. In: Workshop on Electronic and Networking Technologies, pp. 1–5 (2020)

    Google Scholar 

  9. Kolodenkova, A.E., Vereshchagina, S.S.: Information support for decision-making in equipment diagnostics systems based on the network model. Vestnik RGUPS 1, 47–52 (2021)

    Google Scholar 

  10. Simiński, R., Wakulicz-Deja, A.: Circularity in Rule Knowledge Bases Detection Using Decision Unit Approach Monitoring, Security, and Rescue Techniques in Multiagent Systems. Berlin, Heidelberg, pp. 273–79 (2005)

    Google Scholar 

  11. Rybina, G.V., Smirnov, V.V.: Methods and means of verification of knowledge bases in modern expert systems. In: The 8th National Conference on Artificial Intelligence with International Cooperation. Fizmatlit, Moscow 1, pp. 446–54 (2002)

    Google Scholar 

  12. Belov, A.A., Gvozdeva, T.V.: Fundamentals of the Theory of Fuzziness. State Power Engineering University, Ivanovo, p. 119 (2001)

    Google Scholar 

  13. Krivulya, G.F., Shkilb, A.S., Kucherenko, D.E.: Analysis of the correctness of production rules in fuzzy inference systems using quantum models. Automat. Control Syst. Automat. Dev. 165, 42–53 (2013)

    Google Scholar 

  14. Sergienko, M.A.: Methods of designing a fuzzy knowledge base. Bullet. VSU 2, 67–71 (2008)

    Google Scholar 

  15. Gabriel, T.R., Berthold, M.R.: Constructing hierarchical rule systems. In: International Symposium on Intelligent Data Analysis. Berlin, Heidelberg, pp. 76–87 (2003)

    Google Scholar 

  16. Leith, P.: Hierarchically structured production rules. Comput. J. 26(1), 1–5 (1983)

    Article  MathSciNet  Google Scholar 

  17. Patel, A., Jain, S., Shandilya, S.K.: Data of semantic web as unit of knowledge. J. Web Eng. 17(8), 647–674 (2019)

    Article  Google Scholar 

  18. Mohammadpour, R.A., Abedi, S.M., Bagheri, S., Ghaemian, A.: Fuzzy Rule-based Classification System for Assessing Coronary Artery Disease. Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine, pp. 1–8 (2015)

    Google Scholar 

  19. Araujo, A., Krohling, R.: Generating a fuzzy rule based classification system by genetic learning of granularity level using. In: TOPSIS 11th Conference of the European Society for Fuzzy Logic and Technology 1, 482–89 (2019)

    Google Scholar 

  20. Paul, D., Balakrishnan, S., Velusamy, A.: Rule based hybrid weighted fuzzy classifier for tumor data International. J. Eng. Technol. 7, 104–08

    Google Scholar 

  21. Borgi, A., Kalaï, R., Zgaya, H.: Attributes regrouping in fuzzy rule based classification systems: an intra-classes approach. In: The 15th ACS/IEEE International Conference on Computer Systems and Applications. Aqaba, Jordan, pp. 1–7 (2018)

    Google Scholar 

  22. Trillo, J.R., Fernandez, A., Herrera, F.: 2020 HFER: promoting explainability in fuzzy systems via hierarchical fuzzy exception rules. IEEE Int. Conf. Fuzzy Syst., 1–8 (2020)

    Google Scholar 

  23. Ishibuchi, H., Yamamoto, T.: Rule weight specification in fuzzy rule-based classification systems. IEEE Trans. Fuzzy Syst. 13(4), 428–435 (2005)

    Article  Google Scholar 

  24. Jain, R., Abraham, A.: A comparative study of fuzzy classification methods on breast cancer data. Australas. Phys. Eng. Sci. Med. 27(4), 213–218 (2004)

    Article  Google Scholar 

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Acknowledgements

The work was supported by RFBR (Grants No. 19–07-00,195, No. 19–08-00,152, No. 20–38-90,005).

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Authors and Affiliations

  1. Department of Information Technologies, Samara State Technical University, 244, Molodogvardeyskaya street, 443100, Samara, Russian Federation

    A. E. Kolodenkova, S. S. Vereshchagina & V. O. Tuvaeva

Authors
  1. A. E. Kolodenkova
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  2. S. S. Vereshchagina
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  3. V. O. Tuvaeva
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Editor information

Editors and Affiliations

  1. Far Eastern Federal University, Vladivostok, Russia

    Denis B. Solovev

  2. National Technical University of Athens (NTUA), School of Electrical and Computer Engineering, Athens, Greece

    Grigorios L. Kyriakopoulos

  3. Vasil Levski National Military University, Veliko Tarnovo, Bulgaria

    Terziev Venelin

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Kolodenkova, A.E., Vereshchagina, S.S., Tuvaeva, V.O. (2022). Verification of Mixed Production Rules Correctness in Intelligent Systems for Diagnosing Industrial Equipment. In: Solovev, D.B., Kyriakopoulos, G.L., Venelin, T. (eds) SMART Automatics and Energy. Smart Innovation, Systems and Technologies, vol 272. Springer, Singapore. https://doi.org/10.1007/978-981-16-8759-4_38

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