DOI: https://doi.org/10.15802/stp2018/141430

METHOD FOR PLANNING NON-DETERMINED OPERATION PROCESSES OF RAILWAY TECHNICAL SYSTEM PARK

V. V. Skalozub, I. V. Klymenko

Abstract


Purpose. The article is aimed to improve the automated systems operation of the railway technical system parks and switch D.C. electric motors (EMs), taking into account all uncertainties. Methodology. Solution of the problem was obtained through the development of the model and the method for optimal planning for the EMs set operation. The method is based on the information technology with the possibility to assess the parameters of the current and the predicted state of EMs based on their individual models. The models are built both for individual EMs and for the specified groups. The factors of non-determinism in the model are calculated based on the Hurst index. The task of planning is solved as calculating the optimal sequence of the EM facilities services, which provides a minimum of the total expected operating costs. Findings. The analysis of the main known models, the automated technologies and the systems of EM (ASEM) park operation on the basis of the remote monitoring was done in the research. Based on the practice of the EM park maintenance the new category of the analysis objects was proposed – the service group (SG). The new procedure for the processes classification was developed based on using the Hurst index to improve the reliability of EM and SG individual models forecasting. The technological and the economic model for planning the EM parks operation was created. The article presents the results of the developed automated data management system based on the improved model for the operation planning of the D.C. EM parks. The optimal planning model ensures the minimization of the expected operating costs for the EMs operation, due to the selection of the EM groups service queue. The specialized procedure is used to classify non-deterministic EM remote monitoring data during planning, which allows increasing the accuracy of forecasting the object state parameters. Origilnality. The article describes development of the mathematical model and the information technology for the remote monitoring of the railway technical systems park operation, the railway switch EMs based on the formation of EM and SG individual models, as well as on the evaluation of their current and predicted states, taking into account random factors. The proposed model of the optimal planning as the possibility to choose the SG service queue differs by the group maintenance of the EM facilities, as well as application of the specialized procedure for classifying EM monitoring data. Practical value. The practical value of the results is determined by the provision of the new opportunities for the group optimal planning of the EM service based on the criterion of the minimum expected costs. The procedure for the monitoring data classification of the operational processes makes it possible to increase the reliability of the forecasting antipersistent time sequences results. It also provides an interpretation of the observational data classification results based on the need for practical usage.


Keywords


technical system parks; electric motors of switches; operational processes; uncertainty conditions; automated system; monitoring; forecasting; individual process models; optimal planning model; operating costs

References


Razgonov, A. P., Rudenko, A. B., Skalozub, V. V., & Shvets, O. M. (2009). Avtomatizatsiya protsessov diagnostiki elektrodvigateley strelochnykh perevodov v usloviyakh ekspluatatsii. Zaliznychnyi transport Ukrainy, 6, 20-22. (in Russian)

Buryak, S. Y., Gavrilyuk, V. I., Hololobova, O. O., & Kovryhin, M. O. (2015). Remote diagnostics of turnouts state on timing and spectral composition in current curve. Science and Transport Progress. 2(56), 39-57. doi: 10.15802/stp2015/42159 (in Russian)

Instruktsiia z tekhnichnoho obsluhovuvannia prystroiv syhnalizatsii, tsentralizatsii ta blokuvannia. (2009). Kyiv: Ukrzaliznytsia. (in Ukranian)

Maksyshko, N. K., & Perepelytsia, V. O. (2006). Analiz i prohnozuvannia evoliutsii ekonomichnykh system: Monografіya. Zaporizhzhia: Polihraf. (in Ukranian)

Skalozub, V., & Osovik, V. (2014). Individual intelligent models for operating a number of unified railway engineering systems based on the current state parameters. Information and Control Systems at Railway Transport, 6, 8-12. (in Russian)

Skalozub, V. V., Shvets, О. М., & Osovik, V. N. (2014). Methods of Intellectual Transport Systems in Tasks of Management by Parks of Objects of Railway Transport on Current Status. Pytannia prykladnoi matematyky i matematychnoho modeliuvannia, 229-242. (in Russian)

Skalozub, V. V., & Klymenko, Y. V. (2016). Rozvytok protsedur analizu ta prohnozuvannia ne-determinovanykh tekhnoloho-ekonomichnykh protsesiv na osnovi pokaznykiv khaotychnoi dynamiky. Economics: Time Realities, 4(26), 82-90. (in Ukranian)

Skalozub, V. V., Zhukovitskiy, I. V., Klimenko, I. V., & Zaets, А. Р. (2018). Creation of Intellectual Decision Support Systems in a Unified Automated System for Managing Rail Freight in Ukraine. Systemni tekhnolohii: Rehionalnyi mizhvuzivskyi, 3(116), 153-162. (in Russian)

Faggini, M., & Parziale, A. (2012). The Failure of Economic Theory. Lessons from Chaos Theory. Modern Economy, 03(01). doi: 10.4236/me.2012.31001 (in English)

Grabusts, P., Borisov, A., & Aleksejeva, L. (2015). Ontology-Based Classification System Development Methodology. Information Technology and Management Science, 18(1), 129-134. doi: 10.1515/itms-2015-0020 (in English)

Kohonen, T. (2001). Self-Organizing Maps. Berlin; Heidelberg: Springer. (in English)

Kazi, Z., Kazi, L., Radulovic, В., & Bhatt, М. (2016). Ontology-Based System for Conceptual Data Model Evaluation. International Arab Journal of Information Technology, 13(5), 542-551. (in English)

Piegat, A. (1998). Nonregular nonlinear sector modeling. Applied Mathematics and Computer Science, 8(3), 101-123. (in English)

Zhukovyts’kyy, I. (2017). Use of an automaton model for the designing of real-time information systems in the railway stations. Transport problems, 12(4), 101-108. (in English)


GOST Style Citations


  1. Автоматизация процессов диагностики электродвигателей стрелочных переводов в условиях эксплуатации / А. П. Разгонов, А. Б. Руденко, В. В. Скалозуб, О. М. Швец // Залізн. трансп. України. – 2009. – № 6. – С. 20–22.
  2. Дистанционное диагностирование состояния стрелочных переводов по временной характеристике и спектральному составу токовой кривой / С. Ю. Буряк, В. И. Гаврилюк, О. А. Гололобова, М. А. Ковригин // Наука та прогрес транспорту. – 2015. – № 2 (56). – С. 39–57. doi: 10.15802/stp2015/42159
  3. Інструкція з технічного обслуговування пристроїв сигналізації, централізації та блокування : ЦШ 0060. – Київ : Укрзалізниця, 2009. – 111 с.
  4. Максишко, Н. К. Аналіз і прогнозування еволюції економічних систем : монографія / Н. К. Максишко, В. О. Перепелиця. – Запоріжжя : Поліграф, 2006. – 236 с.
  5. Скалозуб, В. В. Индивидуальные интеллектуальные модели для эксплуатации парка однородных железнодорожных технических систем на основе параметров текущего состояния / В. В. Скалозуб, В. Н. Осовик // Інформ.-керуючі системи на залізн. трансп. – 2014. – № 6. – С. 8–12.
  6. Скалозуб, В. В. Методы интеллектуальных транспортных систем в задачах управления парками объектов железнодорожного транспорта по текущему состоянию / В. В. Скалозуб, О. М. Швец, В. Н. Осовик // Питання прикладної математики і математичного моделювання : зб. наук. пр. / Дніпропетр. нац. ун-т ім. О. Гончара. – Дніпропетровськ, 2014. – С. 229–242.
  7. Скалозуб, В. В. Розвиток процедур аналізу та прогнозування недетермінованих технолого-економічних процесів на основі показників хаотичної динаміки / В. В. Скалозуб, И. В. Клименко // Економіка: реалії часу. – 2016. – № 4 (26). – С. 82–90.
  8. Создание интеллектуальных систем поддержки принятия решений в единой автоматизированной системе управления грузовыми железнодорожными перевозками Украины / В. В. Скалозуб, И. В. Жуковицкий, И. В. Клименко, А. П. Заец // Системні технології : регіон. міжвуз. зб. наук. пр. – Дніпро, 2018. – № 3 (116). – С. 153–162.
  9. Faggini, M. The failure of economic theory. Lessons from chaos theory / M. Faggini, A. Parziale // Modern Economy. – 2012. – Vol. 03. – Iss. 01. doi: 10.4236/me.2012.31001
  10. Grabusts, P. Ontology-Based Classification System Development Methodology / P. Grabusts, A. Borisov, L. Aleksejeva // Information Technology and Management Science. – 2015. – Vol. 18. – Iss. 1. – Р. 129–134. doi: 10.1515/itms-2015-0020
  11. Kohonen, T. Self-Organizing Maps / T. Kohonen. – Berlin ; Heidelberg : Springer, 2001. – 501 р.
  12. Ontology-Based System for Conceptual Data Model Evaluation / Z. Kazi, L. Kazi, B. Radulovic, M. Bhatt // International Arab Journal of Information Technology. – 2016. – Vol. 13, No. 5. – P. 542–551.
  13. Piegat, A. Nonregular nonlinear sector modeling / A. Piegat // Applied Mathematics and Computer Science. – 1998. – Vol. 8, No. 3. – P. 101–123.
  14. Zhukovyts’kyy, I. Use of an automaton model for the designing of real-time information systems in the railway stations / I. Zhukovyts’kyy // Transport problems. – 2017. – Vol. 12. – Iss. 4. – P. 101–108.




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