The development of high-speed rail in China has changed the transportation pattern in China, and has also profoundly affected various fields such as economy, politics, culture, and so on. China’s high-speed railway has gradually formed a network from scratch, from point to line, and from line to line. As a “infrastructure craze”, high-speed railway lines run through the country and are densely distributed in the north and south regions. With the help of technology, it has also gnawed off the hard bone of “extremely cold weather”.
Since the beginning of winter, cold wave weather has repeatedly occurred in Northeast China, with the lowest temperature around minus 50 degrees Celsius. In this extremely cold environment, a high-speed train is still running at full speed. Among them, the newly put into operation alpine smart bullet train Fuxing in the northernmost alpine region of China has passed the severe cold test relying on multiple “antifreeze” technologies.
When working in extremely cold regions, there may be problems such as snow accumulation on the underbody, antifreeze cracking of the vehicle body structure at low temperatures, and antifreeze blockage of the circulation system. Trains may idle and slip at low temperatures, ultimately affecting operational safety.
In response to these problems, China Railway Harbin Bureau Group Co., Ltd. has developed a set of operational safety measures to cope with severe cold, ice and snow weather relying on advanced technology. With the support of artificial intelligence technology, the team has developed a set of intelligent image recognition technology that can automatically identify the ice and snow conditions of the bogie. After the operation of the multiple unit is completed and put into storage, low pressure warm water can be used to spray key parts to quickly peel off and melt the ice and snow on the bogie.
In terms of efficiency, it used to take 16 people and 4 hours to complete the ice melting and snow removal of a group of multiple unit trains. It only takes two people to operate for one hour, reducing the time for multi unit deicing operations and effectively accelerating train turnover efficiency.
In addition to “optimizing” the vehicle body, artificial intelligence technology can also improve the operation line: It is reported that natural disasters and foreign matter monitoring systems installed along the high-speed railway, coupled with various sensors, can identify wind speed, snow thickness, etc., and “guide” trains to switch to different “speeds” for different snow and wind conditions.
In addition to applications in extreme weather, artificial intelligence is also playing an increasingly important role in daily maintenance. Generally, when riding on high-speed railways, we often see electrical equipment with numerous pillars and spider webs. Its professional name is catenary. They deliver “electric fuel” to the multiple units. The faster the EMU operates, the higher the requirement for the “healthy” state of the catenary.
In order to reduce potential safety hazards and improve operational levels, artificial intelligence robots have been introduced into high-speed railway lines to provide accurate contact network image data and handle potential safety hazards. The intelligent analysis system has passed data testing, feedback, and error correction for over 30 high-speed railways, over 40000 kilometers, and over 1.1 billion components, forming a large database of OCS equipment defects. The automated analysis capability covers 2160 defects and 514 parts of various high-speed trains, with an analysis efficiency of 200km/day.
With the help of the 4C catenary inspection vehicle, the main components, structures, and key parts of the catenary equipment are imaged and collected with high accuracy in all directions, and then the image data is handed over to the “AI robot”. This can automatically identify and summarize equipment defects from the massive image data, improve the quantity and speed of defect detection, and also liberate analysts from image screening work, focusing on the distribution rules of defects Defect cause investigation and defect rectification tracking.
Currently, the 4C catenary inspection vehicle on the Beijing-Shanghai high-speed railway can collect up to 3 million device images per quarter. Taking 8000 pictures per person per day as an example, more than 20 people need to work overtime for 20 days. With the support of “AI Robot”, two analysts can complete the analysis of the same 3 million images in only 10 days, increasing the efficiency by 20 times.