Research on the Design and Modeling of a Railway Brake Shoe Replacement System Based on NFC and Intelligent Optimization Algorithms
DOI:
https://doi.org/10.62051/40rvmw84Keywords:
NFC technology; Industrial Internet of Things (IIoT); Genetic Algorithm (GA); Machine learning.Abstract
This research addresses the issues of low efficiency, high error rates in traditional manual railway brake shoe replacement, and the mismatch between existing automated equipment and actual needs due to insufficient technical adaptability. It designs an intelligent management and control system integrating NFC technology and intelligent optimization algorithms, adopting a hierarchical architecture (perception, transmission, decision-making layers). NFC realizes automatic data acquisition; SVM and GAM models achieve data matching and traceability; a hybrid GA-PSO algorithm optimizes scheduling. Experimental verification shows the system significantly improves recognition accuracy, scheduling efficiency, and anomaly response speed. Innovations include an NFC-based full-link data acquisition pathway, an SVM-KNN integrated model for accurate data alignment, and a GA-PSO hybrid mechanism for optimal resource allocation. It shifts from manual to algorithm-driven operations, providing a promotable paradigm for digital transformation of railway operation and maintenance.
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[1] Dilek, Esma, Özgür Talih, and Türksel Kaya Bensghir. "An Overview of Paradigm Shift Dynamics in Transportation: Use of Artificial Intelligence in Intelligent Transportation Systems in Türkiye." Acta Infologica 8.1 (2024): 71-87.
[2] Ai, Bo, et al. "5G key technologies for smart railways." Proceedings of the IEEE 108.6 (2020): 856-893.
[3] Bešinović, Nikola, et al. "Artificial intelligence in railway transport: Taxonomy, regulations, and applications." IEEE Transactions on Intelligent Transportation Systems 23.9 (2021): 14011-14024.
[4] Qin Y, Cao Z, Sun Y, et al. Research on active safety methodologies for intelligent railway systems [J]. Engineering, 2023, 27: 266-279.
[5] Min B W. Improved fault prediction algorithm of high-speed EMUs based on PHM technology [J]. International Journal of Contents, 2023, 19 (2): 100-111.
[6] Zhao W, Zhou L, Han C. A Hybrid Optimization Approach Combining Rolling Horizon with Deep-Learning-Embedded NSGA-II Algorithm for High-Speed Railway Train Rescheduling Under Interruption Conditions [J]. Sustainability, 2025, 17 (6): 2375.
[7] Hossain M N, Rahman M M, Ramasamy D. Artificial Intelligence-Driven Vehicle Fault Diagnosis to Revolutionize Automotive Maintenance: A Review [J]. CMES-Computer Modeling in Engineering & Sciences, 2024, 141 (2).
[8] liu Y, Li P, Feng B, et al. Research on digital twin technology and its application in intelligent operation and maintenance of high-speed railway infrastructure [J]. Railway Sciences, 2024, 3 (6): 746-763.
[9] Zhao, Jingyi, Chunhai Gao, and Tao Tang. "A review of sustainable maintenance strategies for single component and multicomponent equipment." Sustainability 14.5 (2022): 2992.
[10] Bortolini, Marco, et al. "Adaptive automation assembly systems in the industry 4.0 era: A reference framework and full–scale prototype." Applied Sciences 11.3 (2021): 1256.
[11] Yingyue, W. A. N. G. "MISMATCH OF LABOR SUPPLY AND DEMAND AND THE PATH OF INDUSTRIAL UPGRADING IN THE CONTEXT OF DIGITAL ECONOMY." Asian Interdisciplinary and Sustainability Review 13.2 (2024): 62-69.
[12] Abdulkarem, Mohammed, et al. "Wireless sensor network for structural health monitoring: A contemporary review of technologies, challenges, and future direction." Structural health monitoring 19.3 (2020): 693-735.
[13] Costa, Filippo, et al. "A review of RFID sensors, the new frontier of internet of things." Sensors 21.9 (2021): 3138.
[14] Li, Yuanyuan, et al. "Machine learning and optimization for production rescheduling in Industry 4.0." The International Journal of Advanced Manufacturing Technology 110.9 (2020): 2445-2463.
[15] Liu, Anbang, et al. "Integrating machine learning and mathematical optimization for job shop scheduling." IEEE Transactions on Automation Science and Engineering 21.3 (2023): 4829-4850.
[16] Chen, Chunlei, et al. "Deep learning on computational‐resource‐limited platforms: A survey." Mobile Information Systems 2020.1 (2020): 8454327.
[17] Adepoju A H, Austin-Gabriel B, Eweje A, et al. Framework for automating multi-team workflows to maximize operational efficiency and minimize redundant data handling [J]. IRE Journals, 2022, 5 (9): 663-664.
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