A new monitoring system was developed recently by scientists from the Hefei Institutes of Physical Science (HFIPS) of the Chinese Academy of Sciences. The system is aimed at ensuring communication and operation safety in the railway section by transmitting early warnings with the help of fiber detection technology.
Rail transportation has a highly significant role in the busy transportation network in China, parallel to the quick economic growth of the country. The railway network is managed in sections, and so is the signal distribution.
Thus, the existing communication system can normally inform the section in which the train is located, but no one is aware of the accurate position of the train inside the section, which might result in severe injuries or even death, particularly for construction workers working on the railway track.
To analyze this issue, scientists suggested an all-inclusive solution that combines several technologies.
Distributed detection technology was employed by the researchers to detect and receive the vibration signal transmitted from optical fiber laid near the railway track, which is the raw data. Artificial intelligence was then used for processing big data as an enormous amount of the raw data required very fast processing.
Eventually, the early warning could be transmitted to each terminal, such as APP, computer and acousto-optic alarm, etc. networked by the cloud platform, which also acted as the database.
The suggested solution seems to be perfect, but the actual surrounding was highly complex than the researchers could visualize. A railway section measuring about 30 km was opened to the team by the China Railway Shanghai Group Co., Ltd for real testing, which made the researchers experience several difficulties.
As far as the real operation environment was concerned, there were lots of hindrances responsible for issues of signal missing, troubles in identifying, location and direction, determination, and so on.
A period of two years was spent by the researchers in the laboratory and beside the railway track solving these difficulties one at a time. Threshold determination was employed to find the target signal from other interference factors. To gain accurate positioning, they resolved it by making use of map correlation positioning and field calibration and survey.
Ultimately, in the project reviewing meeting, the researchers were delighted to get positive comments from the reviewing experts, based on the optimal result of the real environment test.
The researchers felt that the project has achieved its goal for the moment. However, they will not stop here but will strive more to improve the data processing to achieve a warning well in advance. The team is also waiting to extend their test railway section to about 300 km and upgrade the system to supervise the railway track itself.