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Article

  • Title

    Improving the accuracy of dynamic mass calculation

  • Authors

    Dashchenko Oleksandr F.
    Kolomiets Leonid V.
    Lymarenko Oleksandr M.

  • Subject

    MACHINE BUILDING. PROCESS METALLURGY. MATERIALS SCIENCE

  • Year 2015
    Issue 2(46)
    UDC 620.1.082.13-187+681.264.3.08
    DOI 10.15276/opu.2.46.2015.05
    Pages 19-23
  • Abstract

    With the acceleration of goods transporting, cargo accounting plays an important role in today's global and complex environment. Weight is the most reliable indicator of the materials control. Unlike many other variables that can be measured indirectly, the weight can be measured directly and accurately. Using strain-gauge transducers, weight value can be obtained within a few milliseconds; such values correspond to the momentary load, which acts on the sensor. Determination of the weight of moving transport is only possible by appropriate processing of the sensor signal. The aim of the research is to develop a methodology for weighing freight rolling stock, which increases the accuracy of the measurement of dynamic mass, in particular wagon that moves. Apart from time-series methods, preliminary filtration for improving the accuracy of calculation is used. The results of the simulation are presented.

  • Keywords dynamic mass, signal, rail transport, weight, accuracy, autocovariance function
  • Viewed: 2085 Dowloaded: 6
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  • References

    Література
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    References
    1.    Orlova, A. and Boronenko, Yu. (2006). The Anatomy of Railway Vehicle Running Gear. In S. Iwnicki, Handbook of Railway Vehicle Dynamics (pp. 39—83). Boca Raton: Taylor & Francis.
    2.    Cheng, L., Zhang, H. and Li, Q. (2007). Design of a capacitive flexible weighing sensor for vehicle WIM system. Sensors, 7(8), 1530—1544.
    3.    Roberts, S.J. and Penny, W.D. (2002). Variational Bayes for generalized autoregressive models. IEEE Transactions on Signal Processing, 50(9), 2245—2257.
    4.    Orobey, V.F., Dashchenko, A.F. and Limarenko, A.M. (2013). Boundary element method in the problems with unstable solutions. Odes’kyi Politechnichnyi Universytet. Pratsi, 2, 27—31.
    5.    Alsuwaidan, B.N., Crassidis, J.L. and Yang Cheng. (2011). Generalized multiple-model adaptive estimation using an autocorrelation approach. IEEE Transactions on Aerospace and Electronic Systems, 47(3), 2138—2152.
    6.    Oussalah, M. and De Schutter, J. (2000) Adaptive Kalman filter for noise identification. In P. Sas, D. Moens (Eds.), Proceedings of International Conference on Noise and Vibration Engineering (ISMA25) (pp. 1225–1232). Leuven, Belgium: Katholieke Universiteit Lueven.

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