دانشگاه تربیت دبیر شهید رجائی

  اخبار و اعلانات

 آمار

تعداد نشریات13
تعداد شماره‌ها200
تعداد مقالات2,008
تعداد مشاهده مقاله2,680,173
تعداد دریافت فایل اصل مقاله1,937,364
Moradi, M., Havangi, R.. (1402). Estimation of Wheel-Rail Adhesion Force Using Traction System Behavior. فناوری آموزش, 12(1), 271-282. doi: 10.22061/jecei.2023.9935.664
M. Moradi; R. Havangi. "Estimation of Wheel-Rail Adhesion Force Using Traction System Behavior". فناوری آموزش, 12, 1, 1402, 271-282. doi: 10.22061/jecei.2023.9935.664
Moradi, M., Havangi, R.. (1402). 'Estimation of Wheel-Rail Adhesion Force Using Traction System Behavior', فناوری آموزش, 12(1), pp. 271-282. doi: 10.22061/jecei.2023.9935.664
Moradi, M., Havangi, R.. Estimation of Wheel-Rail Adhesion Force Using Traction System Behavior. فناوری آموزش, 1402; 12(1): 271-282. doi: 10.22061/jecei.2023.9935.664

Estimation of Wheel-Rail Adhesion Force Using Traction System Behavior

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 19، دوره 12، شماره 1، فروردین 2024، صفحه 271-282    اصل مقاله (1.43 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2023.9935.664
نویسندگان
M. Moradi؛ R. Havangi*
Faculty of Electrical Engineering and Computer, University of Birjand, Birjand, Iran.
تاریخ دریافت: 23 مرداد 1402،  تاریخ بازنگری: 23 آبان 1402،  تاریخ پذیرش: 13 آذر 1402 
چکیده
Background and Objectives:‎ Traction system and adhesion between wheel and rail are fundamental aspects in rail transportation. Depending on the vehicle's running conditions, different levels of adhesion are needed. Low adhesion between wheel and rail can be caused by leaves on the line or other contaminants, such as rust or grease. Low adhesion can occur at any time of year especially in autumn, resulting in disruptions to passenger journeys. Increased wheel-rail adhesion for transit rail services results in better operating performance and system cost savings. Deceleration caused by low adhesion, will extend the braking distance, which is a safety issue. Because of many uncertain or even unknown factors, adhesion modelling is a time taking task. Furthermore, as direct measurement of adhesion force poses inherent challenges, state observers emerge as the most viable choice for employing indirect estimation techniques. Certain level of adhesion between wheel and rail leads to reliable, efficient, and economical operation.
Methods:‎ This study introduces an advantageous approach that leverages the behavior of traction motors to provide support in achieving control over wheel slip and adhesion in railway applications. The proposed method aims to enhance the utilization of existing adhesion, minimize wheel deterioration, and mitigate high creep levels. In this regard, estimation of wheel-rail adhesion force is done indirectly by concentrating on induction motor parameters as railway traction system and dynamic relationships.  Meanwhile, in this study, we focus on developing and applying the sixth-order Extended Kalman Filter (EKF) to create a highly efficient sensorless re-adhesion control system for railway vehicles.
Results: ‎EKF based design is compared with Unscented Kalman Filter (UKF) based and actual conditions and implemented in Matlab to check the accuracy and performance ability for state and parameter estimation. Experimental results showed fast convergence, high precision and low error value for EKF.
Conclusion:‎ The proposed technique has the capability to identify and assess the current state of local adhesion, while also providing real-time predictions of wear. Besides, in combination with control methods, this approach can be very useful in achieving high wheel-rail adhesion performance under variable complex road conditions
کلیدواژه‌ها
Extended Kalman Filter؛ Adhesion Model؛ Railway Traction, Torque, Estimation
مراجع
[1] C. Schwarz, A. Keck, “Simultaneous estimation of wheel-rail adhesion and brake friction behaviour,” IFAC, 53: 8470-8475, 2020.

[2] R. Lewis, G. Trummer, K. Six, J. Stow, H. Alturbeh, P. Shackleton, B. Bryce,  L. Buckley Johnstone, “Leaves on the line: Characterizing leaf based low adhesion on railway rails,” Tribol. Int., 185: 108529, 2023.

[3] H. Chen, T. Furuya, S. Fukagai, S. Saga, J. Ikoma, K. Kimura, J. Suzumura, “Wheel slip/slide and low adhesion caused by fallen leaves,” Wear, 446-447: 203187, 2020.

[4] M. Watson, B. White, J. Lanigan, T. Slatter, R. Lewis, “The co- mposition and friction reducing properties of leaf layers,” Proc. Math. Phys. Eng. Sci., 476 (2239), 2020.

[5] J. Zhou, M. Wu, C. Tian, et al., “Experimental investigation on wheel–rail adhesion characteristics under water and large sliding conditions,” Ind. Lubric. Tribol., 73(2): 366-372, 2021.

[6] Y. Lyu, E. Bergseth, U. Olofsson “Open system tribology and influence of weather condition,” Sci. Rep. , 6: 32455, 2016.

[7] M. Harmon, R. Lewis “Review of top of rail friction modifier tribology,” Tribol. Mater. Surface Interfac., 10(3): 150-162, 2016.

[8] M. Shen., Y. Qin, D. Ji, et al. “Role of ambient temperature in the adhesion and damage characteristics of wheel/rail interface during rolling-sliding contact,” Wear, 506-507: 204458, 2022.

[9] H. Chen, H. Tanimoto, “Experimental observation of temp-erature and surface roughness effects on wheel/rail adhesion in wet conditions,” Int. J. Rail Transport., 6(2): 101-112, 2018.

[10] U. Olofsson, Y. Lyu, “Open system tribology in the wheel–rail contact—a literature review,” Appl. Mech. Rev. 69(6): 60803, 2017.

[11] I. Yasuoka, T. Henmi, Y. Nakazawa, I. Aoyama, "Improvement of re-adhesion for commuter trains with vector control traction inverter," in Proc. Power Conversion Conference - PCC '97, 1: 51-56, 1997.

[12] Y. Matsumoto, N. Eguchi, A. Kawamura, “Novel re-adhesion control for train traction system of the "Shinkansen" with the estimation of wheel-to-rail adhesive force,” in Proc. IECON'01. 27th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.37243), 2: 1207-1212, 2001.

[13] K. Zhao, P. Li, Ch. Zhang, J. He, Y. Li, T. Yin, “Online accurate estimation of the wheel-rail adhesion coefficient and optimal adhesion antiskid control of heavy-haul electric locomotives based on asymmetric barrier lyapunov function,” J. Sensors, 2018: Article ID 2740679, 2018.

[14] R. Bibi, B. S. Chowdry, R. A. Shah, “PSO based localization of multiple mobile robots employing LEGO EV3,” in Proc. 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET): 1-5, 2018.

[15] S. Shrestha, Q. Wu, M. Spiryagin, “Review of adhesion es-timation approaches for rail vehicles,” Int. J. Rail Transport., 7(2): 79-102, 2019.

[16] X. Fang, S. Lin, Z. Yang , F. Lin, H. Sun, L. Hu “Adhesion control st-rategy based on the wheel-rail adhesion state observation for high-speed trains,”  Electronics, 7(5): 70, 2018.

[17] B. Liu, T. X. Mei, S. Bruni, “Design and optimisation of wheel–rail profiles for adhesion improvement,” Veh. Syst. Dyn., 54(3): 429-444, 2016.

[18] Y. Chen, H. Dong, J. Lu, X. Sun, L. Guo, “A super-twisting-like algorithm and Its application to train operation control with optimal utilization of adhesion force,” IEEE Trans. Intell. Transpor. Syst., 17(11): 3035-3044, 2016.

[19] M. Yamashita, T. Soeda, “Anti-slip re-adhesion control method for increasing the tractive force of locomotives through the early detection of wheel slip convergence,” in Proc. 17th European Conference on Power Electronics and Applications: 1-10, 2015.

[20] R. Rizzo, D. Iannuzzi, “Indirect friction force identification for application in traction electric drives,” Math. Comput. Simul.,  60(3-5): 379-387, 2002.  

[21] I. Hussain, T. X. Mei, R. T. Ritchings, “Estimation of wheel– rail contact conditions and adhesion using the multiple model approach,” Veh. Syst. Dyn., 51(1): 32-53, 2013.

[22] G. Charles, R. Goodall, R. Dixon, “Model-based condition monitoring at the wheel-rail interface,” Veh. Syst. Dyn., 46(1): 415-430, 2008.

[23] F. Orderud, “Comparison of kalman filter estimation approa-ches for state space models with nonlinear measurements,” in Proc. Scand. Conf. Simul., 194: 157-162, 2005.

[24] Y. Zhao, B. Liang, “Re-adhesion control for a railway single wheelset test rig based on the behaviour of the traction motor,” Veh. Syst. Dyn., 51(8): 1173-1185, 2013.

[25] S. Wang, J. Xiao, J. Huang, H. Sheng, “Locomotive wheel slip detection based on multi-rate state identification of   motor load torque,”J. Franklin Inst., , 353(2): 521-540, 2016.

[26] P. D. Hubbard, C. Ward, R. Dixon, R. Goodall,  “Verification of model based adhesion estimation in the wheel-rail interface,” Chem. Eng. Trans., 33: 757-762, 2013.

[27] A. Onat, P. Voltr, M. Lata, “An unscented Kalman filter-based rolling radius estimation methodology for railway vehicles with traction,” Proc. Inst. Mech. Eng., Part F: J. Rail Rapid Transit, 232(6): 1686-1702, 2018.

[28] S. Jafarzadeh, C. Lascu, M. Fadali, “Square root unscented Kalman Filters for state estimation of induction motor drives,” IEEE Trans. Ind. Appl., 49(1): 92-99, 2013.

[29] S. Jafarzadeh, C. Lascu, M. Fadali, “State estimation of induction motor drives using the unscented Kalman filter,” IEEE Trans. Ind. Electron., 59(11): 4207-4216, 2012.

[30] B. Akin, U. Orguner, A. Ersak, M. Ehsani, “Simple derivative free nonlinear state observer for sensorless AC drives,” IEEE/ASME Trans. Mech., 11(5): 634-643, 2006.

[31] M. Barut, S. Bogosyan, M. Gokasan, “Speed-sensorless estimation for induction motors using extended Kalman filters,” IEEE Trans. Ind. Electron., 54(1): 272-280, 2007.

[32] M. Spiryagin, P. Wolfs, C. Cole, V. Spiryagin, “Design and simulation of heavy haul locomotives and trains,” CRC Press, Boca Raton, FL, USA, 2016.

[33] O. Polach, “A fast wheel-rail forces calculation computer,”  Veh. Syst. Dyn. Suppl, 33(1): 728-739, 1999.

[34] J. Kalker, “On The rolling contact of two elastic bodies in the presence of dry friction,”  Wear, 11: 303, 1967.

 

آمار
تعداد مشاهده مقاله: 156
تعداد دریافت فایل اصل مقاله: 174


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب