AI-Driven Framework for Enhancing Safety in Railway Maintenance-of-Way (MoW) Systems
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I1P160Keywords:
Artificial Intelligence, Railway Mow Systems, Safety-Critical Systems, Sensor Simulation, Hazard Detection, Cyber-Physical SystemsAbstract
Modern railway Maintenance-of-Way (MoW) equipment increasingly relies on software-intensive architectures and sensor-driven cyber-physical systems to ensure safe operation in proximity to personnel and active rail infrastructure. Ensuring operational safety in such environments remains a significant challenge due to the limited availability of real-world failure data and the inherent risks associated with field testing under hazardous conditions. This paper presents an AI-driven framework aimed at improving the safety assurance of software-controlled systems deployed in railway MoW environments. The proposed approach integrates high-fidelity sensor simulation with advanced AI models to evaluate system behavior across a wide range of operational and hazardous scenarios, including rare and previously unobservable safety-critical events. The framework enables early identification of unsafe system states, enhances anomaly detection capabilities, and reduces reliance on costly and potentially dangerous field validation activities. Experimental evaluation demonstrates measurable improvements in safety-related performance metrics, highlighting the effectiveness of the proposed framework.
References
[1] J. Smith et al., “AI-Based Safety Systems in Railways,” IEEE Trans. Intelligent Transportation Systems, 2022.
[2] A. Kumar and R. Gupta, “Sensor Fusion for Railway Safety,” IEEE Access, 2021.
[3] P. Wang et al., “Deep Learning for Anomaly Detection,” IEEE Transactions on Neural Networks, 2020.
[4] European Railway Agency, “Safety Standards for Railway Systems,” 2023.
[5] S. Li et al., “Simulation-Based Validation of Safety-Critical Systems,” IEEE Systems Journal, 2022.
[6] O. Gurrapu, “Perception-Driven Path Planning Strategies for Safe Autonomous Vehicles”, IJAIDSML, vol. 7, no. 1, pp. 324–326, Mar. 2026,
[7] J. V. Suman et al., "Real-Time EEG-Based Drowsiness Detection Using Deep Learning Algorithms," 2025 7th International Conference on Energy, Power and Environment (ICEPE), Sohra (Cherrapunjee), India, 2025, pp. 1-5
[8] G. Tummalapalli, O. Gurrapu, K. N. Kumar, J. Venkata Suman, A. V. Rao and M. Prabhu, "Deep Learning Approaches for Enhancing Image Classification Accuracy in Medical Imaging," 2025 Devices for Integrated Circuit (DevIC), Kalyani, India, 2025, pp. 16-21
[9] J. L. Mazher Iqbal, O. Gurrapu, S. P S, R. K. R, R. Jayanthi and T. MB, "Implementing Machine Learning for Early Detection and Prognostic Modeling of Chronic Diseases," 2025 International Conference on Computing for Sustainability and Intelligent Future (COMP-SIF), Bangalore, India, 2025, pp.
[10] K. Krishnakumar, O. Gurrapu, R. P, P. M. Krishnammal, H. Q. Khan and N. Amane, "Improving Medical Imaging Diagnostics with Deep Convolutional Networks for Early Detection and Treatment," 2025 International Conference on Computing for Sustainability and Intelligent Future (COMP-SIF), Bangalore, India, 2025, pp.
[11] O. Gurrapu and P. Kaluvala, "Deep Learning-based Object identification in Ocean Environment by Convolutional Neural models," 2025 International Conference on NexGen Networks and Cybernetics (IC2NC), Erode, India, 2025, pp. 911-915
[12] O. Gurrapu and J. V. Suman, "A Machine Learning Framework for Fault Detection in IoT Enabled Smart Sensor Networks," 2025 Global Conference on Information Technology and Communication Networks (GITCON), Belagavi, India, 2025, pp. 1-6
[13] O. Gurrapu, P. R, I. Swathi, S. R. Kurukuntla, A. Vani and S. Rao Sura, "AI-Powered Intrusion Detection Systems for Cloud Networks," 2025 Second International Conference on Networks and Soft Computing (ICNSoC), Vadlamudi, India, 2025, pp. 179-184
[14] V. Painuly, O. Gurrapu, W. H. Jebaselvi, U. Abdalov, Y. Noushad and V. C. Gandhi, "AI-Enhanced Collision Detection for Autonomous Drones Using LiDAR and Neural Network," 2025 Second International Conference on Networks and Soft Computing (ICNSoC), Vadlamudi, India, 2025, pp. 564-568
[15] R. Sivaranjani, B. S. Priya, O. Gurrapu, P. Kadiri, N. S. Chandana and J. V. Suman, "Image Classification using Quantum Convolutional Neural Network (QCNN)," 2025 International Conference on Metaverse and Current Trends in Computing (ICMCTC), Subang Jaya, Malaysia, 2025, pp. 1-6
[16] models," 2025 International Conference on NexGen Networks and Cybernetics (IC2NC), Erode, India, 2025, pp. 911-915
[17] R. Sivaranjani, B. S. Priya, O. Gurrapu, P. Kadiri, N. S. Chandana and J. V. Suman, "Image Classification using Quantum Convolutional Neural Network (QCNN)," 2025 International Conference on Metaverse and Current Trends in Computing (ICMCTC), Subang Jaya, Malaysia, 2025, pp. 1-6.
