Developed in collaboration with the Fraunhofer Research Institute, Soniq employs AI to analyse serious rail defects and AR to display results clearly for operators. As well as detecting any irregularities inside the rail, the system also detects squats, head-checks and rail-base corrosion.
The data is permanently saved for later analysis and sent to the office via a SIM-card module, thus assisting asset management and maintenance optimisation to increase track availability.
Raw data collected during rail inspections and displayed using B-scans and camera images document the rail’s condition at the time of the inspection. Integration with Microsoft HoloLens enhances the inspection data and shows the operator a spatially accurate virtual overlay superimposed on the real rail. The intelligent integration of proven ultrasound technology with existing infrastructure enables easier interpretation and classification of the data. The learned algorithms option allows serious rail defects to be automatically identified and reported.
Findings can be incorporated into the company’s digitalised process chains to serve as evidence if problems occur. Long-term access to ultrasound data also produces knowledge about damage progression allowing for optimised rail maintenance measures.
