Keywords: railway inspection, railway maintenance, artificial intelligence, railway automation, life-cycle cost, predictive maintenance.

Artificial intelligence and automation are becoming central to railway inspection and maintenance, particularly for large networks such as China's railway system. The analysis links network-scale maintenance pressure, automated inspection platforms, non-destructive testing, smart sensing and data-driven decision support. Artificial intelligence creates value when defect recognition is connected to maintenance action rather than treated as an isolated classification task. Robots, on-vehicle systems, ground penetrating radar, ultrasonic inspection, smart sleepers and internet-of-things monitoring should operate within a closed inspection-to-maintenance loop. China provides a useful setting because its conventional and high-speed railway networks require inspection methods that are low-cost, repeatable and scalable. Representative technologies are organised by asset class, data type, interpretation task and maintenance output. Practical barriers include data quality, sensor calibration, limited labelled defects, cross-line generalisation, safety assurance and life-cycle cost justification. The framework supports a shift from isolated intelligent inspection tools to system-level railway maintenance automation.

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