Improving the process of driving a locomotive through the use of decision support systems
DOI:
https://doi.org/10.15587/1729-4061.2016.80198Keywords:
driving a locomotive, decision making, intelligent system, knowledge base, fuzzy classifierAbstract
The process of driving a train was represented in the form of fuzzy situations, given in a table. The conformity between all possible situations and a set of driving decisions was established. The table size is determined by the number of situations which, in turn, depends on the degree of concretization of values. An algorithm of actions of a locomotive driver when driving a train is presented in the form of fuzzy probabilistic graph. Fuzzy numbers, the values of which are recorded in the matrix graph, represent the weights of transitions between vertices. The choice of decision by a locomotive decision support system (DSS) is carried out using the utility criterion. The training system is implemented with the use of the fuzzy classifier that represents fuzzy knowledge base, the input of which receives signals about current state of the traction rolling stock and of the environment. The model of dynamic knowledge base was obtained.
As a result of analysis of existing types of intelligent systems, hierarchies, and algorithms of their work, taking into account the working conditions of locomotive crews and railway transport as a whole, the parameters for locomotive DSS were developed. We defined the minimal time it takes for a locomotive driver to make a decision about driving a train and to identify emergency situations. The functions of person that directly affect the efficiency and safety of the locomotive and require support using the intelligent systems were determined. The results of the work allow implementing intelligent DSS in modern locomotives. This will enhance the level of safety and efficiency of driving a train.
References
- Railway safety statistics (2015). Eurostat. Statistics Explained. Available at: http://ec.europa.eu/eurostat/statistics-explained/index.php/Railway_safety_statistics (Last accessed: 02.02.2016).
- Volkovskiy, D. (2013). Systems of automatic driving of trains and traffic safety. Evraziya Vesti XII. Available at: http://www.eav.ru/publ1.php?publid=2013-12a15 (Last accessed: 10.12.2015).
- Conventional Automatic Train Protection (ATP) (2015). Siemens AG. Available at: http://www.mobility.siemens.com/mobility/global/en/urban-mobility/rail-solutions/rail-automation/automatic-train-control-system/conventional-automatic-train-protection-atp/pages/conventional-automatic-train-protection-atp.aspx (Last accessed: 23.12.2015).
- A new generation for driverless automated transit systems (2016). Bombardier Inc. Available at: http://www.bombardier.com/en/transportation/products-services/rail-control-solutions/mass-transit-solutions/cityflo-650.html (Last accessed: 24.12.2015).
- Alstom to supply automatic train control system to Santiago de Chile metro’s line 1 (2012). ALSTOM. Available at: http://www.alstom.com/press-centre/2010/1/Alstom-to-supply-automatic-train-control-system-to-Santiago-de-Chile-metros-line-1-20100120 (Last accessed: 10.12.2015).
- Wang, Z. (2005). Study on the structure design and optimization for RITS. China Academic of Railway, 13, 89.
- Yan, M. (2006). Study on the structure design for RITS. China Academic of Railway, 11, 166.
- Fomin, O. V. (2015). Increase of the freight wagons ideality degree and prognostication of their evolution stages. Scientific Bulletin of National Mining University, 2, 68–76.
- Goswami, S., Mehjabin, S., Kashyap, P. A. (n.d.). Driverless Metro Train with Automatic Crowd Control System. Intelligent Applications for Heterogeneous System Modeling and Design, 76–95. doi: 10.4018/978-1-4666-8493-5.ch004
- Potekhin, A. I., Branishtov, S. A., Kuznetsov, S. K. (2014). Supervisory control of the railway system based on Petri nets. XII all-Russia meeting on control problems VCPU-2014, 4956–4965.
- El-Fakih, K., Simao, A., Jadoon, N., Maldonado, J. C. (2016). An Assessment of Extended Finite State Machine Test Selection Criteria. Journal of Systems and Software. doi: 10.1016/j.jss.2016.09.044
- Sales, D. O., Correa, D. O., Fernandes, L. C., Wolf, D. F., Osório, F. S. (2014). Adaptive finite state machine based visual autonomous navigation system. Engineering Applications of Artificial Intelligence, 29, 152–162. doi: 10.1016/j.engappai.2013.12.006
- Hahanov, V., Kaminska, M., Fomina, E. (2006). Testability Analysis of Digital Design Verification. 2006 International Biennial Baltic Electronics Conference, 171–175. doi: 10.1109/bec.2006.311090
- Filippenko, I. G. (2015). Vzaimodeystvuyuschie neyroavtomatyi i neyroavtomatno-vyichislitelnyie strukturyi [Interactive neuroantomy and nanoautomation-computational structures]. Kyiv, Ukraine: Caravel, 440.
- Tarasov, V. A., Gerasimov, B. M., Levin, I. A., Korneychuk, V. A. (2007). Intellektualnie systemi podderzhki prinyatiya resheniy: teoriya, sintez, effektivnost [Intelligent Decision Support Systems: theory, synthesis efficiency]. Kyiv: MAKNS, 336.
- But'ko, T. V., Gorobchenko, O. M. (2015). Modelyuvannya keruyuchoyi diyalnosti mashunista locomotiva za dopomogoyu teorii nechitkih grafiv[Modeling the management of locomotive driver with the help of fuzzy graphs]. Visnuk DNUZT, 2, 88–96.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York, USA: Springer, 738.
- Theodoridis, S., Koutroumbas, K. (2006). Pattern Recognition. 3rd edition. London, UK: Academic Press, 631.
- Demin, D. A. (2013). Nechetkaya klasterizaciya v zadache postroeniy modeley «sostav-svoystvo» po dannim passivnogo experimenta v usloviyah neopredelennosti [Fuzzy Clustering in the problem of model building «structure - property» according to the passive experiment in conditions of uncertainty]. Problemy mashinostroeniya, 15–23.
- Melyhov, A. N., Bershteyn, L. S., Korovin, S. Ya. (1990). Situacionnie sovetueschiye systemi s nechetkoy logikoy [Situational council system with fuzzy logic]. Moscow, Russia: Gl. Red. Fiz. Mat. Lyt., 272.
- Rottshteyn, A. P., Shtovba, C. D. (1997). Nechetkaya nadezhnost algorytmycheskyh processov [Fuzzy reliability of algorithmic processes]. Vinnytsa: Contynent, 142.
- Gorobchenko, O. M. (2010). Vyznachennya imovirnosti vynyknennya transportnoyi podii v locomotyvnomu gospodarstvi [Determining the potential traffic accident in the locomotive sector]. DNUZT, 35, 48–51.
- Madsen, A. L., Kjaerulff, U. B., Kalwa J. (2005). Applications of Probabilistic Graphical Models to Diagnosis and Control of Autonomous Vehicles. The Second Bayesian Modeling Applications Workshop, 12.
- Raskyn, L. G., Seraya, O. V. (2008). Nechetkaya matematyka. [Fuzzy Math]. Kharkiv: Parus, 352.
- Olkkonen, E. A. (1997). Modeli predstavleniya znaniy v yazikovyh intelektualnyh obuchayuchih systemah [Models of knowledge representation language in intelligent tutoring systems]. Works of PGU, 6, 168–182.
- Gorobchenko, O. M. (2011). Korreguvannya funkcii mashinista locomotyva za dopomogoyu system pidtrimki priynyatih rishenn’ [Editing functions locomotive driver using decision support systems]. Locomotiv-inform, 5, 4–5.
- Shtovba, S. D. (2007). Proektirovaniye nechetkyh system sredstvamy MATLAB [Design of fuzzy systems MATLAB tools]. Moscow: Goryachaya linia, 288.
- Gorobchenko, O. M. (2013). Rozrobka matematichnoi modeli dynamichnoi bazi znan’ dlya intelektualnogo keruvannya locomotyvom [Development of a mathematical model of dynamic knowledge bases for intelligent management engine]. Zbirnyk naukovyh praz DIZT, 33, 189–192.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2016 Eduard Tartakovskyi, Oleksandr Gorobchenko, Artem Antonovych
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.