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
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