AI-Driven Fail Operational Safety in Wire Control Systems
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I1P113Keywords:
AI, Wire Control System, Fail Operational Safety, Predictive Maintenance, Anomaly Detection, Redundancy Management, Sensor FusionAbstract
In the current paper, we discuss the topic of artificial intelligence (AI) implementation to improve fault-operational safety in wire control systems, which are widely used in aerospace and industrial automation, as well as robotics. Control systems with wires are essential in matters of precision tasks, and a breakdown may have disastrous impacts. Failure alerts can be tracked, identified, and addressed in real time by using AI methods, especially the machine learning ones, sensor fusion, and predictive maintenance systems. The present study explores the current obstacles of wire control systems, the analysis of AI-implemented techniques of predictive faults, and offers a scheme of AI application to the functioning safety standards. To enhance the reliability of systems and higher fault tolerance, the proposed approach is the combination of sensor data analytics, anomaly detection algorithms, and redundancy management. The results of the experiment show that there is a large decrease in the downtime of systems and an increase in the metrics of operational safety. The research results indicate the potential transformational role of AI in real-time safety surveillance and fail operational insurance in wire-controlled procedures
Downloads
References
[1] Wu, W. H., Chen, C. C., Lin, S. L., & Lai, G. (2022). Automatic anomaly detection and processing for long-term tension monitoring of stay cables based on vibration measurements. Intelligent Transportation Infrastructure, 1, liac002.
[2] Kumar, H., Shafiq, M., Kauhaniemi, K., & Elmusrati, M. (2024, October). Artificial Intelligence-Based Condition Monitoring and Predictive Maintenance of Medium Voltage Cables: An Integrated System Development Approach. In 2024, the 10th International Conference on Condition Monitoring and Diagnosis (CMD) (pp. 191-195). IEEE.
[3] Shao, Q., Fan, S., Zhang, Z., Liu, F., Fu, Z., Lv, P., & Mu, Z. (2025). Artificial intelligence in cable fault detection and localization: Recent advances and research challenges. Energies, 18(14), 3662.
[4] Raman, A., Walker, I., Krovi, V., & Schmid, M. (2023). Cable failure tolerant control and planning in a planar reconfigurable cable driven parallel robot. Frontiers in Robotics and AI, 10, 1070627.
[5] Campos, G. H. F., Pacheco, V. M. G., Reis, M. R. C., Rodrigues, C. G., Silva, S. R., Coimbra, A. P., & Calixto, W. P. (2025). Artificial intelligence-driven protocol for secure and standardized maneuver control in electrical substations. Engineering Applications of Artificial Intelligence, 159, 111667.
[6] Elhoseny, M., Rao, D. D., Veerasamy, B. D., Alduaiji, N., Shreyas, J., & Shukla, P. K. (2024). Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance. International Journal of Computational Intelligence Systems, 17(1), 1-19.
[7] Jiang, D., & Wang, Z. (2023). Research on mechanical equipment fault diagnosis method based on deep learning and information fusion. Sensors, 23(15), 6999.
[8] Software for fail-operational systems – disruptive technology for tomorrow’s autonomous vehicles, elektrobit. Online. https://www.elektrobit.com/blog/software-for-fail-operational-systems-in-autonomous-vehicles/
[9] Xia, Z., Ye, F., Dai, M., & Zhang, Z. (2021). Real-time fault detection and process control based on multi-channel sensor data fusion. The International Journal of Advanced Manufacturing Technology, 115(3), 795-806.
[10] Dennler, N., Haessig, G., Cartiglia, M., & Indiveri, G. (2021, June). Online detection of vibration anomalies using balanced spiking neural networks. In 2021 IEEE 3rd international conference on artificial intelligence circuits and systems (aicas) (pp. 1-4). IEEE.
[11] Knowles, M., Baglee, D., & Wermter, S. (2010, October). Reinforcement learning for scheduling of maintenance. In International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 409-422). London: Springer London.
[12] Marugán, A. P., Pinar-Pérez, J. M., & Márquez, F. P. G. (2024). A reinforcement learning agent for maintenance of deteriorating systems with increasingly imperfect repairs. Reliability Engineering & System Safety, 252, 110466.
[13] Zhang, H., Wei, X., Liu, Z., Ding, Y., & Guan, Q. (2025). Condition-based maintenance for multi-state systems with prognostic and deep reinforcement learning. Reliability Engineering & System Safety, 255, 110659.
[14] Rziki, M. H., Hadbi, A. E., Boutahir, M. K., & Abounaima, M. C. (2025). Adaptive Predictive Maintenance and Energy Optimization in Metro Systems Using Deep Reinforcement Learning. Sustainability, 17(11), 5096.
[15] Blanc, S., Bonastre, A., & Gil, P. J. (2009). Dependability assessment of by-wire control systems using fault injection. Journal of Systems Architecture, 55(2), 102-113.
[16] Barua, R. (2024). Robotics, Automation and Computer Numerical Control. Cambridge Scholars Publishing.
[17] Hossain, M., Rahman, M., & Ramasamy, D. (2024). Artificial intelligence-driven vehicle fault diagnosis to revolutionize automotive maintenance: A review. Computer Modeling in Engineering & Sciences, 141(2), 951.
[18] Lin, T., Ren, Z., Zhu, L., Zhu, Y., Feng, K., Ding, W., ... & Beer, M. (2025). A Systematic Review of Multi-Sensor Information Fusion for Equipment Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement.
[19] Xu, Z., & Saleh, J. H. (2021). Machine learning for reliability engineering and safety applications: Review of current status and future opportunities. Reliability Engineering & System Safety, 211, 107530.
[20] Yan, J., Zhang, Y., Su, Q., Li, R., Li, H., Lu, Z., ... & Lu, Q. (2023). Time series prediction based on LSTM neural network for top tension response of umbilical cables. Marine Structures, 91, 103448.
[21] Duer, S., Woźniak, M., Paś, J., Zajkowski, K., Bernatowicz, D., Ostrowski, A., & Budniak, Z. (2023). Reliability testing of wind farm devices based on the mean time between failures (MTBF). Energies, 16(4), 1659.
