Application of AI in Fault Diagnosis of X-ray Inspection Systems Global Operations and Maintenance Experience
Abstract
This article explores the application of Artificial Intelligence (AI) in fault diagnosis for X-ray Global Inspection Systems (GIS). AI
technologies such as machine learning, deep learning, computer vision, and predictive maintenance are revolutionizing the way faults are detected and managed in these systems. By shifting from reactive to proactive maintenance, AI improves system reliability, reduces downtime,
and enhances operational efficiency. Despite challenges such as data quality and system integration, AIs potential in optimizing X-ray system
performance is significant. As AI continues to evolve, its role in maintaining and enhancing the functionality of X-ray GIS will grow, ensuring
long-term operational success.
technologies such as machine learning, deep learning, computer vision, and predictive maintenance are revolutionizing the way faults are detected and managed in these systems. By shifting from reactive to proactive maintenance, AI improves system reliability, reduces downtime,
and enhances operational efficiency. Despite challenges such as data quality and system integration, AIs potential in optimizing X-ray system
performance is significant. As AI continues to evolve, its role in maintaining and enhancing the functionality of X-ray GIS will grow, ensuring
long-term operational success.
Keywords
AI; Fault Diagnosis; X-ray Systems
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DOI: http://dx.doi.org/10.70711/frim.v3i2.6041
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