Artificial intelligence and 5G technology have significantly reduced freight train inspection times at the Xining East Rolling Stock Depot, located along the Qinghai-Xizang Railway, one of China’s most challenging high-altitude rail corridors. According to depot officials, the adoption of intelligent recognition algorithms has shortened inspection times from approximately 25 minutes per train to between three and five minutes, cutting the workload for physical inspections by 70 percent while increasing fault detection accuracy to over 98 percent.

The Qinghai-Xizang Railway, which connects the Xizang autonomous region with China’s national railway network, celebrated its 20th anniversary on July 1. Since its inauguration in 2006, the line has operated through mountainous terrain averaging 3,000 meters above sea level, presenting unique operational difficulties including extreme weather and high-altitude conditions.

Previously, inspections were conducted manually, requiring personnel to walk long distances along the track under harsh environmental conditions. "When the railway first opened, inspectors relied primarily on basic tools and conducted thorough onsite checks despite altitude sickness, freezing temperatures, and intense ultraviolet radiation," said Lan Yuzhu, director of the Freight Car Repair Workshop at the depot.

The transition to automated diagnostics began in 2016 with the deployment of the Trackside Freight Train Defect Detection System (TFDS). High-definition cameras installed along the railway captured thousands of images of passing trains, which were then transmitted indoors via 5G networks to inspection personnel. This allowed for remote analysis of potential defects, significantly reducing the need for direct field inspections.

Currently, AI serves as the primary inspector, automatically identifying over 300 types of faults in critical components such as braking systems, bogies, and couplers. Suspected issues detected by the system are then reviewed by indoor operators, facilitating continuous 24-hour monitoring and diminishing errors associated with manual fatigue.

To address the extreme winter conditions often encountered on the plateau, the depot has also developed specialized snow-melting and deicing technologies for the cameras, combining intelligent heating, hot-air deicing, and automatic snow removal. These measures have decreased false alarms by more than 95 percent in temperatures as low as minus 40 degrees Celsius.

The modernization extends beyond inspections. Maintenance operations have advanced from manual work by veteran mechanics relying on hand tools to automated production lines capable of disassembly, cleaning, flaw detection, assembly, and testing. Improved heating, ventilation, and automated lifting equipment have also enhanced working conditions within the depot.

Furthermore, the railway’s safety infrastructure has evolved from infrared axle temperature monitoring to integrated intelligent monitoring stations. Located at key points including Golmud, Nagchu, and Lhasa, these stations capture temperature, sound, image, and force data in real time, feeding the information into China State Railway Group's big-data platform.

According to Lan, this technological evolution represents a fundamental shift from manual inspection toward intelligent prevention and data-driven safety management. Continued development of AI and big data technologies is expected to contribute to enhanced reliability and safety for the Qinghai-Xizang Railway in the future.