Case Study: Oc’via achieves better geometry defect detection with KONUX AI and IIoT

How Oc’via Maintenance optimizes geometry monitoring with the help of AI and IIoT

Oc’via Maintenance, a service provider for the Oc'via and SNCF rail bypass project, faced challenges in cost-effectively monitoring railway track geometry and identifying the root causes of defects using its traditional inspection wagon. The high cost of frequent manual inspections and a lack of contextual data for diagnosis were their primary limitations. To address this, they partnered with vendor KONUX.

KONUX implemented its IIoT Predictive Maintenance System to provide continuous track monitoring. The solution analyzed sensor data to correlate track tilt changes with contextual events, successfully identifying the root causes of defects. This allowed Oc’via Maintenance to confirm its hypothesis that groundwater levels were a key factor. The results indicate potential savings of three maintenance tampings per area annually and improved track quality without increasing the frequency of costly manual inspections.

Oc’via