Rail Defect Measurement System: Integrating AI and IoT for Predictive Operations
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V2I2P105Keywords:
Rail Defect Measurement, Artificial Intelligence, IoT, Predictive Maintenance, Machine Learning, Smart Sensors, Railway SafetyAbstract
Rail transport plays a significant role in rail traffic worldwide as it is a vital link to the transport infrastructure. However, rail track defects present some challenges, making rail operations inefficient and threatening the safety of trains and travelers. Routine inspections that require physical or sensorial examination and are done periodically may not effectively identify the defects when they are still immature. The higher education of this paper consists of an enhanced Rail Defect Measurement System (RDMS) that involves the use of Artificial Intelligence (AI) and the Internet of Things (IoT) for predictive operations. A feedback system through integrated IoT sensors allows constant tracking of rail conditions, and with help from AI, defect identification, categorization, and prognosis are done in real time. This paper also developed a proposed framework that involves data acquisition using intelligent sensors, data processing through a cloud, and machine learning algorithms for anomaly detection. It not only improves the measurement of the defect but also promotes a predictive maintenance system, which helps reduce time loss and risks to railway systems. From these experiments, one can infer that the investigated system can accurately identify defects such as cracks, wear, and misalignments. Compared with traditional techniques, the effectiveness of the combined AI-IoT technique has been proven. This paper provides a better understanding of possible improvements and further research into five other domains in intelligent rail maintenance
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