Predicting a technical condition of railway automation hardware under conditions of limited statistical data

Authors

DOI:

https://doi.org/10.15587/1729-4061.2017.102005

Keywords:

microprocessor systems, railway automation, Student spread, method of maximum likelihood, microstatistics

Abstract

Here we report a method developed for the prediction of technical equipment of railway automation. It is based on the Student spread, the methods of maximum likelihood and unevenly accurate observations.

Development of the method for prediction was necessitated by a limited experience of operating the microelectronic systems of railway automation by domestic transportation enterprises. This led to a shortage of statistical data on their operation. Thus, the issue of the application of microstatistics for technical diagnosis of respective devices has become relevant.

As a result of the study we established that the basis for prediction may be formed by the principle of violation of the equivalence class of failure-free devices. The existence of a faulty device violates the integrity of the class. This makes it possible as a desired probability of failure of the device to determine the probability of its exiting the corresponding equivalence class. Under conditions of minimal statistical data, this approach has proved its suitability for micro-electronic equipment.

Thus, we obtain the possibility to predict technical condition of microelectronic equipment of railway automation under conditions of shortage of statistical data. The method has several disadvantages associated with deliberate understatement of values of confidence probability of failure-free work of devices. However, it lays the foundation for further improvement in the methodology of technical diagnosis of information-control systems on railway transport. This is executed with regard to the introduction of the newest modifications, not sufficiently tested as yet. 

Author Biographies

Valentin Moiseenko, Ukrainian State University of Railway Transport Feierbakh ave., 7, Kharkiv, Ukraine, 61050

Doctor of Technical Sciences, Professor, Head of Department

Department of specialized computer systems

Oleksandr Kameniev, Ukrainian State University of Railway Transport Feierbakh ave., 7, Kharkiv, Ukraine, 61050

PhD, Associate Professor

Department of automatic and computer remote control of train traffic

Vitalii Gaievskyi, Ukrainian State University of Railway Transport Feierbakh ave., 7, Kharkiv, Ukraine, 61050

Department of specialized computer systems

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Published

2017-06-15

How to Cite

Moiseenko, V., Kameniev, O., & Gaievskyi, V. (2017). Predicting a technical condition of railway automation hardware under conditions of limited statistical data. Eastern-European Journal of Enterprise Technologies, 3(9 (87), 26–35. https://doi.org/10.15587/1729-4061.2017.102005

Issue

Vol. 3 No. 9 (87) (2017): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2017 Valentin Moiseenko, Oleksandr Kameniev, Vitalii Gaievskyi

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