ARTIFICIAL INTELLIGENCE FOR TRANSPORTATION FAULT DETECTION: A SYSTEMATIC REVIEW OF PATTERN RECOGNITION AND PREDICTIVE MAINTENANCE
Keywords:
Artificial Intelligence, Transportation Safety, Fault Detection, Predictive Maintenance, Pattern RecognitionAbstract
This article presents a comprehensive analysis of artificial intelligence applications in transportation fault detection systems, examining the integration of machine learning algorithms in predictive maintenance and pattern recognition. The article investigates the implementation of AI-driven solutions across various transportation domains, including autonomous vehicles, railway systems, and traffic management infrastructure. Through systematic review of current methodologies and case studies, this article identifies key patterns in fault detection mechanisms and evaluates their effectiveness in enhancing transportation safety and reliability. The article demonstrates the transformative potential of AI in revolutionizing traditional fault detection approaches, while highlighting critical challenges in data integration and system optimization. This article contributes to the growing body of knowledge in transportation safety systems by providing insights into the practical applications of AI-based fault detection and offering recommendations for future technological developments in the field. The article implications suggest significant opportunities for improving transportation system reliability through advanced AI applications, while acknowledging the need for continued innovation in addressing current limitations.
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