Simulation Model of Electric Code-Modulated Signal in Russian Systems of Interval Control of Train Movement Based on Track Circuit
Keywords:
Сode-Modulated Signal, Automatic Locomotive Signaling, Machine Learning, Butterworth Digital Filter, Rail Line, Normal Distributed Random Variable, OctaveAbstract
Systems of interval control of train movement Signaling systems, which are currently in service in Russian railways, use the electric track circuit as the main data channel between signals and locomotives. Code-modulated electric signals transferred through that channel are frequently get corrupted which leads to railway traffic delays.
Decoding of the electric signal received from a track circuit can be represented as an image classification problem, and thus the stability of the data channel could be significantly improved.
However, to build such a classifier based on some machine learning algorithm, one needs a large dataset. In this article, a simulation model to synthesize this dataset is proposed.
The structure of the computer model matches the main stages of the electric code-modulated signal generation in a track circuit: code signal generator, rails, locomotive receiver.
Based on code signal generator schematic and waveform diagrams, a generator algorithm is developed. At this stage, we modeled timings of electric code signals according to the specification as well as their random deviations caused by various factors.
The analysis of substitution circuits of the rail line revealed that it has the properties of a low-pass filter. So, the rail line using the Butterworth digital filter with corresponding parameters is modeled. Additionally, at this stage, random noise during transmission was taken into account.
A similar technique is applied for modeling of a locomotive receiver which has a band-pass filter as the first signal processing block.
Thus, the proposed simulation model consists of a set of algorithms which run in series. By varying the parameters of the model, one can synthesize waveform diagrams of the electric code-modulated signal received by the locomotive equipment from a track circuit working in various modes and conditions.
References
2. Leushin V.B., Yusupov R.R. Osobennosti kanalov avtomaticheskoj lokomotivnoj signalizacii magistral'nyh zheleznyh dorog [Features of channels of automatic locomotive signaling of main railways]. Samara State Transport University. 2007. 115 p. (In Russ).
3. Tabunshchikov AK, Gorenbeyn E.V., Stryapkin L.I. [Failures of automatic locomotive signaling. Problems and solutions]. Avtomatika, svyaz', informatika – Automation, communication and Informatics. 2015. vol. 8. pp. 21–22.(In Russ).
4. Bryleev A.M., Pope O., Dmitriev V.S., Kravtsov Y.A., Stepensky B.M. Avtomaticheskaya lokomotivnaya signalizaciya i avtoregulirovka [Automatic locomotive signaling and auto-adjustment]. M.: Transport Publ. 1981. 320 p. (In Russ).
5. Leonov A.A. Tekhnicheskoe obsluzhivanie avtomaticheskoj lokomotivnoj signalizacii [Maintenance of automatic locomotive signaling]. M.: Transport Publ. 1982. 255 p. (In Russ).
6. Shamanov V.I. [The magnetic properties of rail lines and level of interferences for the apparatus of automatic control and telemechanics]. Elektrotekhnika – Russian Electrical Engineering. 2015. vol. 9. pp. 50–54. (In Russ).
7. Lochehin V.S. [Receiving device for automatic cab signalling]. Invention № 2517631 RU. Publ. 27.05.2014. Paper № 15. (In Russ).
8. Argunov I.A. et al. [Device for suppressing impulse noise in input of locomotive receiver ALS]. Invention № 2618616 RU. Publ. 04.05.2017. Paper № 13. (In Russ).
9. Leushin V.B., Yusupov R.R., Blachev K.E. [Receiving device for automatic cab signalling]. Utility model № 165420 RU. Publ. 20.10.2016. Paper № 29. (In Russ).
10. Zasov V.A., Zheleznov D.V., Mitrofanov A.N., Belonogov A.S. [Adaptive noise abatement in automatic cab signaling receivers]. Elektrotekhnika – Russian Electrical Engineering. 2017. vol. 3. pp. 18–22. (In Russ).
11. Rosenberg E.N. [Digital Railway - Nearest Future]. Avtomatika, svyaz', informatika – Automation, communication and Informatics. 2016. vol. 10. pp. 4–7. (In Russ).
12. Bishop C.M. Pattern recognition and machine learning. Springer. 2011. 738 p.
13. Goodfellow I., Bengio Y., Courville A. Deep Learning. MIT Press. 2016. 775 p.
14. Murphy K.P. Machine learning: a probabilistic perspective. MIT Press. 2012. 1104 p.
15. Harrington P. Machine Learning in Action. Manning Publications. 2012. 384 p.
16. Brink H., Richards J., Fetherolf M. Real-world machine learning. Manning Publications. 2016. 264 p.
17. Smith S.W. Digital signal processing: a practical guide for engineers and scientists. Newnes. 2013. 650 p.
18. Shai S.S., Shai B.D. Understanding machine learning: from theory to algorithms. Cambridge University Press. 2014. 410 p.
19. Soroko VI, Fotkina Z.V. Apparatura zheleznodorozhnoj avtomatiki i telemekhaniki [Railway automatics and remote control equipment]. Planeta Publ. 2013. 1048 p. (In Russ).
20. Bryleev A.M., Kravtsov Y.A., Shishlyakov A.V. Teoriya, ustrojstvo i rabota rel'sovyh cepej [Theory, design and operation of track circuits]. M.: Transport Publ. 1978. 344 p. (In Russ).
21. Arkatov V.S., Arkatov Y.V., Kazeev S.V., Obodovskiy Y.V. Rel'sovye cepi magistral'nyh zheleznyh dorog [Track circuits of main railways]. M.: Missia-M Publ. 2006. 496 p. (In Russ).
22. Alexander C.K., Sadiku M. Fundamentals of electric circuits. McGraw-Hill Education. 2016. 992 p.
23. Schubert T.F., Kim E.M. Fundamentals of electronics: book 3: active filters and amplifier frequency response. Morgan & Claypool Publishers. 2016. 294 p.
24. Bishop O. Understand Electronic Filters. Newnes. 1996. 180 p.
25. Lam H.Y-F. Analog and digital filters: design and realization. Prentice Hall. 1979. 632 p.
26. Pultyakov A.V, Skorobogatov M.E. [System analysis of sustainability of automatic locomotive signaling systems]. Sovremennye tekhnologii. Sistemnyj analiz. Modelirovanie – Modern technologies. System analysis. Modeling. 2018. vol. 1. pp. 79–89. (In Russ).
27. Sergienko A.B. Сifrovaya obrabotka signalov [Digital Signal Processing]. BHV-Petersburg Publ. 2011. 768 p. (In Russ).
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