Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm
Autoři:
Xiaoying Yu aff001; Hongsheng Su aff001; Zeyuan Fan aff002; Yu Dong aff001
Působiště autorů:
College of Automation and Electrical Engineering & Key Laboratory of Opto-Technology and Intelligent Control Ministry of Education, Lanzhou Jiaotong University, Lanzhou, Gansu, China
aff001; School of Rail Transportation, Shandong Jiaotong University, Jinan, Shandong, China
aff002
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0226751
Souhrn
An algorithm to predict train wheel diameter based on Gaussian process regression (GPR) optimized using a fast simulated annealing algorithm (FSA-GPR) is proposed in this study to address the problem of dynamic decrease in wheel diameter with increase in mileage, which affects the measurement accuracy of train speed and location, as well as the hyper-parameter problem of the GPR in the traditional conjugate gradient algorithm. The algorithm proposed as well as other popular algorithms in the field, such as the traditional GPR algorithm, and GPR algorithms optimized using the artificial bee colony algorithm (ABC-GPR) or genetic algorithm (GA-GPR), were used to predict the wheel diameter of a DF11 train in a section of a railway during a period of major repairs. The results predicted by FSA-GPR was compared with other three algorithms as well as the real measured data from RMSE, MAE, R2 and Residual value. And the comparisons showed that the predictions obtained from the GPR optimized using FSA algorithm were more accurate than those based on the others. Therefore, this algorithm can be incorporated into the vehicle-mounted speed measurement module to automatically update the value of wheel diameter, thereby substantially reducing the manual work entailed therein and improving the effectiveness of measuring the speed and position of the train.
Klíčová slova:
Algorithms – Optimization – Machine learning algorithms – Artificial intelligence – Covariance – Simulated annealing – Wheels
Zdroje
1. Hu QZ, Tan MJ, Lu HP, Zhu Y. A rough set-based measurement model study on high-speed railway safety operation. Plose one. 2018; 13(6): https://doi.org/10.1371/journal.pone.0197918.
2. Hu XL, Zhao S, Shi F, Huang J, Shan XH. Circuity analyses of HSR network and high-Speed train paths in China. Plos one. 2017; 12(9): https://doi.org/10.1371/journal.pone.0176005.
3. Shebani A, Lwnicki S. Prediction of wheel and rail wear under different contact conditions using artificial neural networks. Wear. 2018; 406–407: 173–184.
4. Luo R, Shi HL, Teng WX, Song CY. Prediction of wheel profile wear and vehicle dynamics evolution considering stochastic parameters for high-speed train. Wear. 2017; 392–393: 126–138.
5. Niu G, Xiong LJ, Qin XX, Pecht M. Fault detection isolation and diagnosis of multi-axle speed sensors for high-speed trains. Mechanical Systems and Signal Processing. 2019; 131: 183–198.
6. Wu XH, Tao HQ, Cai X. Vehicle wheel diameter calibration method based on multi-sensor information fusion in rail transit. Urban Mass Transit. 2015; 6(5): 21–27.
7. Guo ZG, Zhao JB, Ni M. Train speed detection and positioning system based on embedded multi-information fusion. Computer Engineering. 2013; 38(12): 11–15.
8. Liu J, Cai BG, Wang J, Tang T. Study on wheel diameter calibration method in integrated train positioning based on gray theory. China Rail Soc. 2011; 33(5): 54–59.
9. Kang J, Duan ZT, Kang L, Liu Y, Wang C. A short traffic flow prediction method based on Gaussian processes regression. Journal of Transportation Systems Engineering and Information Technology. 2015; 15(4): 51–56.
10. Qiao SJ, Jin K, Han N, Tang CJ, Gesang DJ, Gutierrez LA. Trajectory prediction algorithm based on Gaussian mixture model. Journal of Software. 2015; 26(5): 1048–1063.
11. Tian ZD, Li SJ, Wang YH, Wang XD. Network traffic prediction based on ARIMA with Gaussian process regression compensation. Beijing Univ Posts Telecommun. 2017; 40(6): 1179–1189.
12. Liu KY, Liu BG, Xu C. Intelligent analysis model of slope nonlinear displacement time series based on genetic-Gaussian process regression algorithm of combined kernel function. Chin J Rock Mech Eng. 2009; 28(10): 2128–2134.
13. Li JZ, Meng XR, Wen XX, Kang QY. Network security situation prediction based on Gaussian process optimized by glowworm swarm optimization. Systems Engineering and Electronics. 2015; 37(8): 1887–1893.
14. Ernest P, Mazl R, Preucil L. Train locator using inertial sensors and odometer. Proceedings of the IEEE Intelligent Vehicles Symposium; 2004 June 14–17; Parma, Italy. New Jersey. IEEE Periodicals; 2004.
15. Saab SS, Nasr GE, Badr EA. Compensation of axle-generator errors due to wheel slip and slide. IEEE Trans Veh Technol. 2002; 51(3): 577–587.
16. Liu KY, Fang Y, Liu BG, Xu C. Intelligent deformation prediction model of tunnel surrounding rock based on genetic-Gaussian process regression coupling algorithm. China Rail Soc. 2011; 33(12): 101–106.
17. Rasmussen CE, Williams CKI. Gaussian process for machine learning. 1st ed. Cambridge: MIT Press; 2006.
18. Twomey N, Chen HY, Diethe T, Flach P. An application of hierarchical Gaussian processes to the detection of anomalies in star light curves. Neurocomputing. 2019; 342: 152–163.
19. Awwal AM, Kumam P, Abubakar AB. A modified conjugate gradient method for monotone nonlinear equations with convex constraints. Applied Numerical Mathematics, 2019; 145: 507–520.
20. Luo ZH. Railway vehicle engineering. 1st ed. Changsha: Central South University Press; 2015.
21. Grary FM, Schmidt M. Thermal building modelling using Gaussian processes. Energy Build. 2016; 119: 119–128.
22. He ZK, Liu GB, Zhao XJ, Wang MH. Overview of Gaussian process regression. Control and Decision. 2013; 28(8): 1121–1128.
23. Wahlstrom N, Ozkan E. Extended target tracking using Gaussian processes. IEEE Trans Signal Process. 2015; 63(16): 4165–4178.
24. Mellor J, Grigoras L, Carbonell P, Faulon JL. Semisupervised Gaussian process for automated enzyme search. ACS Synth Biol. 2016; 5(6): 518–528. doi: 10.1021/acssynbio.5b00294 27007080
25. Zhang YP, Gao W, Zhao B. A comprehensive fault detection method for jointless track circuit based on SA algorithm. China Rail Soc. 2017; 39(4): 68–72.
26. Shen WW, LI Y, Yang ZH, Wang XL, Ye X. Attribute reduction with avoiding overfitting. Application Research of Computers. 2019;37(9): Available from: http://kns.cnki.net/kcms/detail/51.1196.TP.20190830.1427.058.html
27. Gan D, Ke DP, Sun YZ, Cui MJ. Short-term Wind Speed Probabilistic Forecasting Based on EEMD and Coupling GA-GPR. Transactions of CHINA Electrotechnical Society. 2015; 30(11): 138–147.
28. Geng XQ, She QS, Han X, Meng M. Classification of Motor Imagery EEG Based on Gaussian Process Optimized with Artificial Bee Colony. Chinese Journal of Sensors and Actuators. 2017; 30(3): 378–384.
29. Zhang L, Liu Z, Zhang JQ, Ren XW. Optimized improved Gaussian process model based on artificial bee colony algorithm. Journal of National University of Defense Technology. 2014; 36(1): 154–160.
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