TrackDefectX at Hancock Iron Ore: maintenance and safety takeaways for rail engineers
Reviewed by Joe Ashwell

First reported on Australian Mining
30 Second Briefing
Hancock Iron Ore is deploying its AI-powered TrackDefectX system to automate defect detection across its heavy-haul rail network, targeting rail breaks, geometry faults and ballast issues that previously relied on manual inspection. Using high-resolution imaging and machine-learning models mounted on in-service rollingstock, TrackDefectX flags anomalies in near real time and feeds them into a central maintenance planning platform. The system, which earned Hancock the 2025 Excellence in IIoT Application award, is expected to cut inspection intervals, reduce unscheduled downtime and support more targeted tamping and rail replacement.
Technical Brief
- Similar AI inspection deployments on heavy‑haul lines could rationalise patrol frequencies while tightening compliance evidence for regulators.
Our Take
Within the 434 Infrastructure stories in our database, only a subset combine iron ore operations with AI or artificial intelligence keywords, so TrackDefectX positions Hancock Iron Ore towards the more data-driven end of Australian rail maintenance practice.
Most of the 1193 tag-matched Product/Safety/Projects pieces focus on mine-site equipment rather than rail corridors, suggesting this kind of dedicated track-defect platform could become a reference model for other heavy-haul iron ore lines in Australia.
With no deal_type flagged for Hancock Iron Ore around TrackDefectX, this looks more like an in-house or tightly controlled deployment than a broad commercial rollout, which may give the company a short-term operational edge before similar tools diffuse across the Pilbara iron ore rail network.
Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.
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