Optimization design for the installation position of hold bracket in railway bogie system by uniform design method

References

• Amirian, M., and F. Schwenker. 2020. “Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability.” IEEE Access 8:123087–123097. doi:10.1109/ACCESS.2020.3007337.
   Web of Science ®Google Scholar
• Bernal, E., M. Spiryagin, and C. Cole. 2019. “Onboard Condition Monitoring Sensors, Systems and Techniques for Freight Railway Vehicles: A Review.” IEEE Sensors Journal 19 (1): 4–24. doi:10.1109/JSEN.2018.2875160.
   Web of Science ®Google Scholar
• Bors, A. G., and I. Pitas. 1996. “Median Radial Basis Function Neural Network.” IEEE Transactions on Neural Networks 7 (6): 1351–1364. doi:10.1109/72.548164.
   PubMedGoogle Scholar
• Chatterjee, K., Z. Ou, F. K. H. Phoa, and H. Qin. 2017. “Uniform Four-Level Designs from Two-Level Designs: A New Look.” Statistica Sinica 27:171–186. doi:10.5705/ss.202015.0230.
   Web of Science ®Google Scholar
• Dumitriu, M. 2022. “Condition Monitoring of the Dampers in the Railway Vehicle Suspension Based on the Vibrations Response Analysis of the Bogie.” Sensors 22 (9): 3290–24. doi:10.3390/s22093290.
   PubMed Web of Science ®Google Scholar
• Fang, K. T., and Y. Wang. 1993. Number-Theoretic Methods in Statistics. London: Chapman & Hall.
   Google Scholar
• Fukagai, S., T. Toyama, T. Tanaka, M. Kuzuta, and H. Doi. 2022. “Visualization of Wheel-Rail Contact Area of Running Vehicle Using Film Sensor.” Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail & Rapid Transit 236 (7): 745–755. doi:10.1177/09544097211041841.
   Web of Science ®Google Scholar
• He, Y., and J. McPhee. 2002. “Design Optimization of Rail Vehicles with Passive and Active Suspensions: A Combined Approach Using Genetic Algorithms and Multibody Dynamics.” Vehicle System Dynamics 37 (sup1): 397–408. doi:10.1080/00423114.2002.11666249.
   Google Scholar
• Hodge, V. J., S. O’Keefe, M. Weeks, and A. Moulds. 2015. “Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey.” IEEE Transactions on Intelligent Transportation Systems 16 (3): 1088–1106. doi:10.1109/TITS.2014.2366512.
   Web of Science ®Google Scholar
• Kampczyk, A., and K. Dybeł. 2021. “The Fundamental Approach of the Digital Twin Application in Railway Turnouts with Innovative Monitoring of Weather Conditions.” Sensors 21 (17): 5757. doi:10.3390/s21175757.
   PubMed Web of Science ®Google Scholar
• Karageorghis, A., and P. Tryfonos. 2019. “Shape Parameter Estimation in RBF Function Approximation.” International Journal of Computational Methods and Experimental Measurements 7 (3): 246–259. doi:10.2495/CMEM-V7-N3-246-259.
   Google Scholar
• Kostrzewski, M., and R. Melnik. 2021. “Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review.” Sensors 21 (14): 4710. doi:10.3390/s21144710.
   PubMed Web of Science ®Google Scholar
• Leclerc, F., and J. Y. Potvin. 1997. “Genetic Algorithms for Vehicle Dispatching.” International Transactions in Operational Research 4 (5–6): 391–400. doi:10.1111/j.1475-3995.1997.tb00094.x.
   Google Scholar
• Lee, C. K., Y. C. Cheng, and C. P. Jiang. 2015. “Explicit Dynamic Finite Element Analysis and Uniform Design with Kriging Interpolation and Optimization to Improve an On-Road Bicycle Frame Undergoing Drop-Mass Impact Test.” Journal of the Chinese Society of Mechanical Engineers 36 (4): 353–361.
   Web of Science ®Google Scholar
• Lophaven, S. Y., H. B. Nielsen, and J. Søndergaard. 2002. DACE: A MATLAB Kriging Toolbox. Report No. IMM-TR-2002-12. Kongens Lyngby, Denmark: Computer programme, Informatics and Mathematical Modelling, Technical University of Denmark.
   Google Scholar
• McLean, P., P. Léger, and R. Tinawi. 2006. “Post-Processing of Finite Element Stress Fields Using Dual Kriging Based Methods for Structural Analysis of Concrete Dams.” Finite Elements in Analysis and Design 42 (6): 532–546. doi:10.1016/j.finel.2005.10.004.
   Web of Science ®Google Scholar
• Niu, H., T. Hou, and Y. Chen. 2022. “Research on Energy-Saving Operation of High-Speed Trains Based on Improved Genetic Algorithm.” Journal of Applied Science and Engineering 26 (5): 663–673. doi:10.6180/jase.202305_26(5).0009.
   Web of Science ®Google Scholar
• Que, Q., and M. Belkin. 2020. “Back to the Future: Radial Basis Function Networks Revisited.” IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (8): 1856–1867. doi:10.1109/TPAMI.2019.2906594.
   PubMed Web of Science ®Google Scholar
• Ranyal, E., A. Sadhu, and K. Jain. 2022. “Road Condition Monitoring Using Smart Sensing and Artificial Intelligence: A Review.” Sensors 22 (8): 3044. doi:10.3390/s22083044.
   PubMed Web of Science ®Google Scholar
• Song, G., G. Xu, Y. Quan, Q. Yuan, and P. A. Davies. 2016. “Uniform Design for the Optimization of Al2O3 Nanofilms Produced by Electrophoretic Deposition.” Surface and Coatings Technology 286:268–278. doi:10.1016/j.surfcoat.2015.12.039.
   Web of Science ®Google Scholar
• Tsunashima, H. 2019. “Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique.” Applied Sciences 9 (13): 2734. doi:10.3390/app9132734.
   Google Scholar
• Wu, Q., Y. Sun, M. Spiryagin, and C. Cole. 2018. “Methodology to Optimize Wedge Suspensions of Three-Piece Bogies of Railway Vehicles.” Journal of Vibration and Control 24 (3): 565–581. doi:10.1177/1077546316645698.
   Web of Science ®Google Scholar
• Yang, Z. 2022. “Optimal Loading Method of Multi Type Railway Flatcars Based on Improved Genetic Algorithm.” Journal of Intelligent Systems 31 (1): 915–926. doi:10.1515/jisys-2022-0025.
   Web of Science ®Google Scholar
• Yoon, H. J. 2013. “Design of Rail Vehicle Weighting System Using Pressure Sensor.” Applied Mechanics and Materials 365–366:741–744. doi:10.4028/www.scientific.net/AMM.365-366.741.
   Google Scholar
• Yudistiro, D., and Y. C. Cheng. 2023. “Optimization and Innovative Design of Dental Implants Under Dynamic Finite Element Analysis.” Journal of the Chinese Institute of Engineers 46 (6): 615–627. doi:10.1080/02533839.2023.2227878.
   Web of Science ®Google Scholar
• Zanelli, F., M. Mauri, F. Castelli-Dezza, E. Sabbioni, D. Tarsitano, and N. Debattisti. 2022. “Energy Autonomous Wireless Sensor Nodes for Freight Train Braking Systems Monitoring.” Sensors 22 (5): 1876. doi:10.3390/s22051876.
   PubMed Web of Science ®Google Scholar