文章基本信息

标题：Predicting a technical condition of railway automation hardware under conditions of limited statistical data
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作者：Valentin Moiseenko ; Oleksandr Kameniev ; Vitalii Gaievskyi 等
期刊名称：Eastern-European Journal of Enterprise Technologies
印刷版ISSN：1729-3774
电子版ISSN：1729-4061
出版年度：2017
卷号：3
期号：9
页码：26-35
DOI：10.15587/1729-4061.2017.102005
语种：English
出版社：PC Technology Center
摘要：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.
关键词：microprocessor systems;railway automation;Student spread;method of maximum likelihood;microstatistics