In a cross-divisional pilot project, voestalpine Signaling Siershahn GmbH—a subsidiary of voestalpine Railway Systems, the global leader for railway infrastructure system solutions—and Logistik Service GmbH (LogServ) are developing a state-of-the-art Visual Train Analysis system: innovative camera analysis for moving wagons.
As an environmentally friendly and safe means of transport, rail freight is an essential factor in making competitive industries fit for the future. Rail freight is therefore becoming increasingly popular, with demand constantly growing. For optimal utilization, rail-bound transport must be efficient and reliable. This also applies to the wagons which are the key freight transport resource. In response, a current cross-divisional voestalpine pilot project is developing a Visual Train Analysis system designed to provide the global rail industry with considerable added value in the future.
Jumping on the bandwagon
Visual Train Analysis (VTA) is an innovative camera-based system for analyzing the condition of moving wagons. Two voestalpine divisions are using VTA to help the rail industry optimize maintenance and make this mode of transport even safer. For this project, voestalpine Signaling Siershahn (Metal Engineering Division) provides the know-how for developing, implementing, and setting up sensor systems for the rail industry, while LogServ (Steel Division) contributes its expertise in operating connecting railroads as well as rail transport companies and in maintaining freight wagons.
"Our ability to work together across divisions and create added value for the entire rail industry is really special."
Visual Train Analysis provides the rail industry with a visual support point with which to automatically assess wagons and track the condition of their wearing parts. Ongoing technical inspections increase the safety of rail traffic. Wagons can also be scheduled for predictive maintenance. Other benefits include optimized warehousing and personnel planning as well as reduced wagon downtime.
An eye on everything
In the future, high-resolution cameras will be used to record potential damage and signs of wear on critical components and to document the condition of the wagons. This analysis will be carried out while the train is moving, without interfering with regular operations. Five industrial cameras, two on each side of the track and one in a raised position, automatically record the moving wagon. They capture the wagon from all perspectives: from the bogie to the body and the roof. Nine infrared flashes ensure that the images are uniformly illuminated during the day and at night. The flash in the near-infrared range is invisible to the human eye, preventing any potential adverse effects. The images are analyzed by algorithms, and the wagon number (UIC identification), revision date, QR codes, and other labels evaluated. Panoramic images are used to assess the general condition of the wagons. The algorithm also recognizes possible hazardous goods labels, whether the wagon is properly closed, and the wheel rim thickness in order to assess wear. This VTA-supported automatic identification lets customers know the location and condition of wagons at all times.
Always on the move
The collaborative project has been running since the fall of 2020, with the first prototype installed at the voestalpine site in Linz in June 2022. The pilot plant is currently being tested for speeds of up to 40 km/h. Speeds of up to 250 km/h are also conceivable, making Visual Train Analysis suitable for high-speed trains. Appropriate hardware is currently being tested in the in-house lab. Since testing began, the error rate has been reduced, the hit rate increased, and the VTA functions further developed. The project is now working on the next development stage for hardware and software. For example, it is possible to detect the thickness of the brake pads, possible cracks in the springs, and missing covers, bolts, and nuts. These are further decisive parameters for making rail transport even safer, more economical, and more reliable.
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