Produced in association with the Fraunhofer Research Institute, Soniq employs artificial intelligence to analyze severe rail issues and augmented reality to present results for operators. As well as identifying any abnormalities inside the rail, the system also recognizes head-checks, rail-based corrosion, and squats.
The data is permanently stored for analysis whenever necessary and transferred to the office through a SIM-card process, thus supporting maintenance optimization and asset management to improve track availability.
Data gathered during rail investigations and presented using camera images and B-scans record the rail’s condition at the investigation time. Integration with Microsoft HoloLens improves the investigation data and tells the operator a spatially precise virtual overlay superimposed on the actual rail. The integration of ultrasound technology with the available infrastructure allows more straightforward diagnosis and classification. The learning algorithms selection allows severe rail errors to be automatically recognized and reported.
Conclusions are incorporated into the digitalized process chains to work as proof if problems occur. Long-term use of ultrasound data also creates knowledge about damage improvement, which allows optimized rail maintenance measures.