Salim Saiful Islam, Quaium Adnan, Kamal Uday, Rahaman Masfiqur, Hossain Mainul, Sakib Nazmul Hasan, Toha Tarik Reza, Alim Al Islam A B M
Next-Generation Computing (NeC) Research Group, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh.
Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka, 1208, Bangladesh.
Sci Rep. 2025 Aug 28;15(1):31728. doi: 10.1038/s41598-025-99404-y.
Derailments, common in developing nations, often result from dislodged or defective rail blocks, leading to substantial property damage and loss of life. Developing an automated real-time wireless sensing system for preventing derailments is a complex challenge, particularly in resource-constrained regions with limited network infrastructure. Existing research has yet to provide a practical solution that effectively addresses the need for long-distance sensing and optimized sensor deployment. This research focuses on developing a comprehensive solution that addresses these challenges. We explore vibration sensing and multi-sensor fusion for accurate rail track detection. Through rigorous experimentation and analysis, we demonstrate a 95% accuracy rate in detecting incoming trains from a distance of 1 km. To optimize sensor deployment, we contrive a multi-objective optimization problem and employ a meta-heuristic approach. We generate effective sensor deployment topologies by considering factors such as cost, vulnerability, and accident statistics. Our findings reveal that optimized deployment can significantly reduce the deployment costs up to 50% while preventing over 65% of potential accidents compared to a brute-force deployment approach. This research offers a promising solution for real-time rail track detection, combining advanced technologies and data-driven optimization. The findings contribute to enhancing railway safety and mitigating the devastating consequences of derailments.
脱轨事故在发展中国家很常见，通常是由松动或有缺陷的铁轨部件导致的，会造成重大财产损失和人员伤亡。开发一种用于预防脱轨的自动化实时无线传感系统是一项复杂的挑战，尤其是在网络基础设施有限的资源受限地区。现有研究尚未提供一种切实可行的解决方案来有效满足长距离传感和优化传感器部署的需求。本研究专注于开发一种全面的解决方案来应对这些挑战。我们探索振动传感和多传感器融合技术以实现精确的铁轨检测。通过严格的实验和分析，我们证明在距离1公里处检测进站列车的准确率达到95%。为了优化传感器部署，我们设计了一个多目标优化问题并采用元启发式方法。我们通过考虑成本、易损性和事故统计等因素生成有效的传感器部署拓扑。我们的研究结果表明，与蛮力部署方法相比，优化部署可显著降低高达50%的部署成本，同时预防超过65%的潜在事故。本研究结合先进技术和数据驱动的优化方法，为实时铁轨检测提供了一个有前景的解决方案。这些研究结果有助于提高铁路安全性并减轻脱轨事故的灾难性后果。
