Науковий вісник НГУ

Forecasting of wear of pads of modernized brake system devices of bogies of freight cars using ARIMA models

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №6 2020

Last Updated on 22 December 2020

Published on 30 November -0001

Hits: 2587

Authors:

V.H.Ravlyuk, orcid.org/0000-0003-4818-9482, Ukrainian State University of Railway Transport, Kharkiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

S.V.Mykhalkiv, orcid.org/0000-0002-0425-6295, Ukrainian State University of Railway Transport, Kharkiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A.V.Rybin, orcid.org/0000-0003-4430-8018, Ukrainian State University of Railway Transport, Kharkiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Ya.V.Derevianchuk, orcid.org/0000-0002-4932-2751, Ukrainian State University of Railway Transport, Kharkiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.A.Plakhtii, orcid.org/0000-0002-1535-8991, Ltd VO OVEN, Kharkiv, Ukraine, -mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2020, (6): 048 - 054

https://doi.org/10.33271/nvngu/2020-6/048

Abstract:

Purpose. The purpose is to create discrete stochastic ARIMA models for forecasting the remaining life of pads of modernized brake rigging (BR) devices of bogies of industrial railway cars.

Methodology. Accounting of statistical data on the wear of pads of typical and modernized BR devices obtained in the relevant studies. On the basis of analytical designs of BR, changes in the junction of the vertical lever with the spacer are proposed. Akaike and Bayesian information criteria are used for selecting the optimal integrated autoregression and moving average model within the Box-Jenkins methodology for forecasting the remaining mileage of pads.

Findings. The ARIMA model was identified, evaluated, and checked for adequacy according to the Akaike and Bayesian information criteria. It is established that abnormal wear of the top of the pads of typical BR devices due to a number of design and operational reasons occurs when the mileage is about 3.5 times less than the forecasted life before the abnormal wear of the top of the pads of modernized BR devices. The forecasted remaining life of the top of the pad of the modernized BR is 3.3 thousand km shorter than that for the bottom of the same pad.

Originality. For the first time, the remaining life of the pads of the modernized BR devices of industrial freight cars was forecasted using discrete stochastic ARIMA models, which require only the availability of discrete values that are recorded during the relevant experimental measurements.

Practical value. The results of the study were verified on experimental rolling stock with modernized devices in the brake systems of bogies. They can be used in the design, upgrade and operation of the brake systems of both the rolling stock which is currently in operation and the new generation of bogies of freight cars.

Keywords: ARIMA model, freight car, wear, forecasting, brake pad, brake rigging

References.

1. Ravlyuk, V.G., Afanasenko, I.M., & Ravliuk, M.G. (2020). Investigation of the geometric parameters of the brake pads of freight cars under hazardous wear. Scienceand Transport Progress, 1(85), 99-118. https://doi.org/10.15802/stp2020/199515.

2. Department of Car Facilities (2019). Analysis of the state of train safety at the Ukrainian railways for 2019. Kyiv: Fast Motion.

3. Ravlyuk,V.G. (2019). Investigation of features of dual wear of pads in brake system of freight cars. Science and Transport Progress, 2(80), 11-126. https://doi.org/10.15802/stp2019/ 166114.

4. Operation Manual for Rolling Stock Brakes at the Ukrainian railways. CT-CB-CL-0015 (2004). Kyiv: Polygraphservice.

5. Vakkalagadda, M.R.K., Srivastava, D.K., Mishra, A., & Racherla, V. (2015). Performance analyses of brake blocks used by Indian Railways. Wear, 328-329, 64-76. https://doi.org/10.1016/j.wear.2015.01.044.

6. Zhang, Y., & Zhang, M. (2018). The application status of unit brakes on metro vehicles in China. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), 3(15), 17-23. https://doi.org/10.9790/1684-1503031723.

7. Vineesh, K.P., Vakkalagadda, M.R.K., Tripathi,A.K., Mishra,A., & Racherla,V. (2016). Non-uniformity in braking in coaching and freight stock in Indian Railways and associated causes. Engineering Failure Analysis, 59, 493-508. https://doi.org/10.1016/j.engfailanal.2015.11.023.

8. Matyjaszek,M., Fernandez,P.R., Krzemien,A., Wodarski,K., & Valverfe,G.F. (2019). Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory. Resources Policy, 61, 283-292. https://doi.org/10.1016/j.resourpol.2019.02.017.

9. Prema,V., & Rao,K.U. (2015). Time series decomposition model for accurate wind speed forecast. Renewables: Wind, Water, and Solar, 2(1), 18. https://doi.org/10.1186/s40807-015-0018-9.

10. Zubenko, D., Kovalenko, A., & Kuznetsov, O. (2015). Analysis of existing approaches to setting the intelligent management systems of transport undertakings. Eastern-European Journal of Enterprise Technologies, 9(78), 17-22. https://doi.org/10.15587/1729-4061.2015.56693.

11. Box,G.E.P., Jenkins,G.M., Reinsel,G.C., & Ljung,G.M. (2016). Time series analysis. Forecasting and control. 5^th edition. New Jersey: Wiley & Sons, 712.

12. Martinez-Alvarez, F., Troncoso, A., Asencio-Cortes, G., & Riquelme, J.C. (2015). Asurvey on data mining techniques applied to electricity-related time series forecasting. Energies, 8(11), 13162-13193. https://doi.org/10.3390/en81112361.

13. Mykhalkiv,S., & Bulba,V. (2019). The forecasting of the technical state of the traction gearboxes of electric trains with discrete stochastic models. Collected Scientific Works of Ukrainian State University of Railway Transport, 188, 23-35. https://doi.org/10.18664/1994-7852.188.2019.206182.

14. Meer van der, D.W., Shepero, M., Svensson, A., Widen,J., & Munkhammar, J. (2018). Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Procesesses. Applied Energy, 213, 195-207. https://doi.org/10.1016/j.apenergy.2017.12.104.

15. Pavlyukov, A.E., Cherepov, O.V., & Shalupin, I.A. (2017). Freight wagon brake pads: analysis of damage and factors affecting the creation of braking force. Bulletin of the Ural State University of Communication, 4(36), 4-11.

16. Sinitsyn,V.V., & Kobishchanov, V.V. (2020). Braking system for bogie successive braking of freight cars. Bulletin of the Bryansk State Technical University, 3(88), 21-28. https://doi.org/10.30987/1999-8775-2020-3-21-28.

17. Tuluzin, S.V., & Gorskij, D.V. (2015). Performance assessment of the brake rigging of the bogie of the freight car at various stages of wear of pads and wheels. Bulletin VNIIZHT, 2, 38-44.

18. Hyndman,R.J., & Athanasopoulos,G. (2018). Forecasting: Principles and Practice (2^nd ed.). OTexts.

• < Prev
• Next >