Development of a Diagnostic Data Fusion Model of the Electrical Equipment at Industrial Enterprises

  • Anna E. KolodenkovaEmail author
  • Elena A. Khalikova
  • Svetlana S. Vereshchagina
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1156)


Continuous monitoring and diagnostics of the equipment technical condition are needed to improve reliability, to prevent possible failures, to ensure the service life extension of electrical equipment (EE) at industrial enterprises. In the present work, the authors suggest to use a diagnostic data fusion model developed for the EE technical condition diagnosis. To test the model, a scenario for searching the EE failure state was made and implemented. A diagnostic data fusion model is necessary to process the increasing amount of information produced by various EEs for subsequent analysis. The proposed data fusion model uses the levels of the Joint Directors of Laboratories (JDL) model, Data Mining technology, the ontology storage and EE diagnostics and prediction models and methods based on probabilistic statistical methods and soft computing methods. A detailed description of a fault detection model for EE at an oil company is considered. The developed diagnostic data fusion model will make it possible to identify EE faulty states and failures, as well as to increase the efficiency of making diagnostic decisions under the conditions of heterogeneous data obtained from a lot of EEs.


Electrical equipment Heterogeneous data Data fusion Soft computing methods 



The work was supported by RFBR (Grants No. 19-07-00195, No. 19-08-00152).


  1. 1.
    Abramov, O.V.: Monitoring and forecasting of the technical condition of systems of responsible appointment. Inform. Control Syst. 2(28), 4–15 (2011)Google Scholar
  2. 2.
    Voloshin, A.A., Voloshin, E.A.: Forecasting the technical condition of the equipment and managing the stability of the energy system through technology of the internet of things for monitoring in electric networks of low. Int. J. Humanit. Nat. Sci. 12, 128–134 (2017)Google Scholar
  3. 3.
    Horoshev, N.I., Eltishev, D.K.: Integrated assessment and forecasting of technical condition of the equipment of electrotechnical complexes. Inform. Control Syst. 4(50), 58–68 (2016)Google Scholar
  4. 4.
    Kovalev, S.M., Kolodenkova, A.E., Snasel, V.: Intellectual technologies of data fusion for diagnostics technical objects. Ontol. Designing 9(1), 152–168 (2019)CrossRefGoogle Scholar
  5. 5.
    Alofi, A., Alghamdi, A., Alahmadi, R., Aljuaid, N., Hemalatha, M.: A review of data fusion techniques. Int. J. Comput. Appl. 167(7), 37–41 (2017)Google Scholar
  6. 6.
    Khramshin, V.R., Nikolayev, A.A., Evdokimov, S.A., Kondrashova, Y.N., Larina, T.P.: Validation of diagnostic monitoring technical state of iron and steel works transformers. In: IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW), pp. 596–600 (2016)Google Scholar
  7. 7.
    Saushev, A.V., Sherstnev, D.A., Shirokov, N.V.: Analysis of methods for diagnosing high-voltage apparatus. Bull. Admiral Makarov State Univ. Maritime Inland Shippin 9(5), 1073–1085 (2017)Google Scholar
  8. 8.
    Bulac, C., Tristiu, I., Mandis, A., Toma, L.: On-line power systems voltage stability monitoring using artificial neural networks. In: International Symposium on Advanced Topics in Electrical Engineering, pp. 622–625 (2015)Google Scholar
  9. 9.
    Nakamura, E.F., Loureiro, A.A., Frery, A.C.: Information fusion for wireless sensor networks: methods, models, and classifications. ACM Comput. Surv. 39(3), 55 (2007)CrossRefGoogle Scholar
  10. 10.
    Pareek, S., Sharma, R., Maheshwari, R.: Application of artificial neural networks to monitor thermal condition of electrical equipment. In: 3rd International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), pp. 183–187 (2017)Google Scholar
  11. 11.
    Eltyshev, D.K.: On the development of intelligent expert diagnostic system for assessing the conditions of electrical equipment. Syst. Methods Technol. 3(35), 57–63 (2017)Google Scholar
  12. 12.
    Modern methods of diagnostics and assessment of the technical condition of electric power equipment. Accessed 10 May 2019
  13. 13.
    Vdoviko, V.P.: Methodology of the high-voltage electrical equipment diagnostics system. Electricity 2, 14–20 (2010)Google Scholar
  14. 14.
    Garcia, J., Rein, K., Biermannn, J., Krenc, K., Snidaro, L.: Considerations for enhancing situation assessment through multi-level fusion of hard and soft data. In: 19th International Conference on Information Fusion (FUSION), Heidelberg, pp. 2133–2138 (2016)Google Scholar
  15. 15.
    Kolodenkova, A., Khalikova, E., Vereshchagina, S.: Data fusion and industrial equipment diagnostics based on information technology. In: International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), Vladivostok, Russia, pp. 1–5 (2019)Google Scholar
  16. 16.
    Kolodenkova, A.E., Dolgiy, A.I.: Diagnosing of devices of railway automatic equipment on the basis of methods of diverse data fusion. Adv. Intell. Syst. Comput. 875, 277–283 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Anna E. Kolodenkova
    • 1
    Email author
  • Elena A. Khalikova
    • 1
  • Svetlana S. Vereshchagina
    • 1
  1. 1.Samara State Technical UniversitySamaraRussia

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