Diagnosing of Devices of Railway Automatic Equipment on the Basis of Methods of Diverse Data Fusion

  • Anna E. Kolodenkova
  • Alexander I. Dolgiy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 875)


In the this work it is emphasized that fusion of the diverse data obtained from sources of primary information (sensors, the measuring equipment, systems, subsystems) for adoption of diagnostic decisions at a research of malfunctions of devices of railway transport, is one of the main problems. The generalized scheme of fusion of diverse data reflecting features of this process is considered. Also classification of levels, modern methods of fusion of diverse data in the conditions of incomplete, indistinct basic data is considered. Approach to fusion of diverse data on malfunction of the devices of railway transport received from a set of various sensors with use of the theory of Dempster-Shafer for the purpose of their integration and development of uniform diagnostic decisions for the benefit of end users is offered. Rationing of the weight coefficients reflecting ability of sensors, and fusion of values of mass of probability is the cornerstone of the offered approach. A numerical example for a decision-making illustration at diagnostics of malfunctions of devices of railway transport in the conditions of uncertainty is reviewed.


Fusion of diverse data Normalization of data Approaches and methods of fusion of data Railway transport 


  1. 1.
    Bevilacqua, M., Tsourdos, A., Starr, A., Durazo-Cardenas, I.: Data fusion strategy for precise vehicle location for intelligent self-aware maintenance systems. In: International Conference on Intelligent Systems, Modelling and Simulation, pp. 76–81 (2015)Google Scholar
  2. 2.
    Dolgiy, A.I., Dolgiy, I.D., Kovalev, V.S., Kovalev, S.M.: Intellectual models of the nonlinear filtration of data in fiber-optical systems of gathering and processing of the primary information. News Volgograd State Tech. Univ. 9, 63–68 (2011). (in Russian)Google Scholar
  3. 3.
    Reimer, C., Hinüber, E.L.: INS/GNSS/Odometer data fusion in railway applications. In: Symposium Inertial Sensors and Systems, Karlsruhe, Germany, p. 14 (2016)Google Scholar
  4. 4.
    Veloso, M., Bentos, C., Camara Pereira, F.: Multi-sensor data fusion on intelligent transport systems. MIT Portugal Transportation Systems Working Paper Series, p. 18 (2009)Google Scholar
  5. 5.
    Ben Brahim, A.: Solving data fusion problems using ensemble approaches, p. 104 (2010)Google Scholar
  6. 6.
    Polastre, J., Hill, J., Culler, D.: Versatile low power media access for wireless sensor networks, pp. 95–107 (2004)Google Scholar
  7. 7.
    Nowak, R., Mitra, U., Willett, R.: Estimating inhomogeneous fields using wireless sensor networks. IEEE J. Sel. Areas Commun. 22, 999–1006 (2004)CrossRefGoogle Scholar
  8. 8.
    Zhao, J., Govindan, R., Estrin, D.: Residual energy scans for monitoring wireless sensor networks. In: IEEE Wireless Communications and Networking Conference, vol. 1, pp. 356–362. IEEE, Orlando (2002)Google Scholar
  9. 9.
    Krishnamachari, B., Iyengar, S.: Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Trans. Comput. 53, 241–250 (2004)CrossRefGoogle Scholar
  10. 10.
    Pasha, E., Mostafaei, H.R., Khalaj, M., Khalaj, F.: Fault diagnosis of engine using information fusion based on Dempster-Shafer theory. J. Basic Appl. Sci. Res. 2(2), 1078–1085 (2012)Google Scholar
  11. 11.
    Mostafaei, H.R., Khalaj, M., Khalaj, F., Khalaj, A.H., Makui, A.: Engine fault diagnosis decision-making with incomplete information using Dempster-Shafer theory. J. Basic Appl. Sci. Res. 2(1), 105–113 (2012)Google Scholar
  12. 12.
    OtmanBasir, X.Y.: Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory. Inf. Fusion 8, 379–386 (2007)CrossRefGoogle Scholar
  13. 13.
    Kolodenkova, A.E.: The process modeling of project feasibility for information management systems using the fuzzy cognitive models. J. Comput. Inf. Technol. 6(114), 10–17 (2016). (in Russian)Google Scholar
  14. 14.
    Dempster, D., Shafer, G.: Upper and lower probabilities induced by a multi-valued mapping. Ann. Math. Stat. 38, 325–339 (1967)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Samara State Technical UniversitySamaraRussia
  2. 2.Rostov State Transport UniversityRostov-on-DonRussia

Personalised recommendations