AI slashes inspection times at Xining repair depot

Artificial intelligence and 5G data transmission have reduced freight train inspection times by about 80 percent at the Xining East Rolling Stock Depot, marking a shift from manual labor to automated diagnostics on the high-altitude Qinghai-Xizang Railway.

According to data released by the depot, the integration of intelligent recognition algorithms has cut the time required to inspect a standard freight train from 25 minutes to between three and five minutes. The digital system has reduced physical inspection workloads by 70 percent while raising diagnostic fault detection accuracy to more than 98 percent.

The technological overhaul came as the railway marked its 20th anniversary. Opened on July 1, 2006, it is the first rail link connecting the Xizang autonomous region to China's national network.

"When a train passes a monitoring station, images are transmitted in real time through 5G," said Jiang Xiaoling, a diagnostic inspector at the depot. "The AI system automatically identifies more than 300 types of defects, and we focus on verifying the suspected problems instead of inspecting every carriage by hand."

The current system automatically flags specific fault characteristics across critical mechanical components, including braking systems, bogies, and couplers, before routing the high-definition images to indoor operators for secondary verification.

The automated process contrasts with the operational logistics during the railway's first decade of service, when trackside personnel routinely walked up to 20 kilometers per day along sections averaging 3,000 meters above sea level to conduct manual checks.

"When the railway first opened, an inspector's toolkit consisted of little more than a hammer, a flashlight and a tool bag," said Lan Yuzhu, director of the Freight Car Repair Workshop at the Xining East Rolling Stock Depot.

"Altitude sickness, freezing temperatures and intense ultraviolet radiation were part of the job," Lan said. "Every potential fault had to be identified on site, one component at a time."

The depot began transitioning from labor-intensive trackside checks in 2016 with the deployment of the Trackside Freight Train Defect Detection System, or TFDS. High-definition cameras installed along the railway started capturing thousands of images of passing freight trains and transmitting them to an indoor inspection platform, allowing inspectors to analyze the images on screen rather than examining every carriage in the field.

Instead of conducting full inspections manually, field workers only needed to verify the locations identified by the system, dramatically improving efficiency while reducing their exposure to the plateau's harsh environment, according to the depot.

Today, AI has become the system's "first inspector". Once images arrive from trackside cameras, intelligent recognition algorithms automatically identify fault characteristics before forwarding suspected defects to inspectors for confirmation. The technology operates around the clock, reducing the risk of missed faults caused by fatigue while making inspections faster and more accurate.

"The biggest change isn't simply that the inspections are faster," Jiang said. "It's that we've moved from spending hours outdoors checking trains to completing full-train diagnostics indoors with AI as our partner."

Engineers have also tailored the technology to one of the plateau railway's toughest challenges — extreme winter weather. According to Lan, specially developed snow-melting and deicing devices have been installed at trackside monitoring stations along the railway. Combining intelligent heating, hot-air deicing and automatic snow removal, the system keeps camera windows clear in temperatures as low as-40 C, reducing equipment false alarms by more than 95 percent.

The digital transformation extends beyond train inspections. Lan said veteran mechanics once gathered around simple workbenches, relying on hand tools, manual measurements and experience to overhaul freight-car components.

Today, automated production lines complete everything from disassembly and cleaning to flaw detection, assembly and testing, while modern heating, ventilation and automated lifting equipment have significantly improved working conditions.

The railway's safety network has evolved just as dramatically. In the early years, monitoring depended largely on infrared systems that tracked axle temperatures. Today, integrated intelligent monitoring stations at key locations including Golmud, Nagchu and Lhasa collect temperature, sound, image and force data simultaneously, uploading the information in real time to China State Railway Group's big-data platform.

"What we've witnessed over the past two decades is a fundamental shift from relying primarily on human inspection to intelligent prevention powered by technology," Lan said. "As AI and big data continue to develop, they will help keep the Qinghai-Xizang Railway safer and more reliable for years to come."

Zheng Jinran in Beijing contributed to this story.

Contact the writers at palden_nyima@chinadaily.com.cn