Case Study: Autonomous TLO Bin Inspection
Pilbara Iron Ore Fixed Plant Inspection: Eliminating Confined Space Risk in TLO Bins
The Asset: Transfer Load Out (TLO) Bins
Transfer Load Out (TLO) bins sit at the heart of high-throughput ore handling circuits. At this Pilbara site, four large-format TLO bins receive continuous iron ore flow at volume, with internal HDPE and rubber composite liners exposed to constant abrasion, impact, and thermal cycling, conditions that degrade liner material progressively and, if unchecked, catastrophically.
A liner failure, even a localised wear-through, carries serious consequences: structural damage to the bin shell, ore stream contamination, and unplanned shutdowns lasting multiple days. With the site processing in excess of 80 million tonnes per annum, a single 24-hour unplanned stoppage represents millions of dollars in lost production.
TLO bins and chutes are not low-priority assets. They are high-consequence, high-wear, and historically, chronically under-inspected.
The Safety Challenge: Confined Space and Work‑at‑Height Exposure
The old approach: rope access, confined space entry, limited data
Before engaging Nexxis, the site relied on a rope access methodology for all internal TLO bin inspections. A team of certified rope access technicians would enter each bin from above, suspended on lines, to visually assess liner condition and manually probe wear rates in areas they could physically reach.
This approach created three compounding problems.
Safety exposure every inspection cycle. Each bin inspection required a confined space entry permit, working at heights approval, and a standby rescue team on site. A typical inspection mobilised six to eight personnel, with significant exposure to fall risk, atmospheric hazard, and manual handling, for every single inspection event.
Inspections were slow and expensive. A single bin took 12 to 14 hours to inspect, including rigging setup, access, inspection, and demobilisation. Running all four bins required a planned four-day shutdown window, with contractor costs, rope access crews, safety standby, permit administration, and associated downtime, running at over $550,000 per annual inspection cycle.
The data produced wasn’t fit for predictive maintenance. Despite the cost and risk, rope access inspections produced qualitative visual reports and spot ultrasonic thickness (UT) readings from whatever areas the technicians could safely reach. There was no spatial referencing, no repeatable baseline, no wear-rate trending, and no coverage of the bin surfaces inaccessible from a suspended position.
The result: inspections were limited to twice per year (not because of asset risk, but because of access cost), and the maintenance team was making decisions based on incomplete, unmapped, untrended data. The operation was managing a high-consequence asset reactively.
The Robotic Inspection Solution
A modular robotic inspection system – no personnel entry required
Nexxis deployed a fully integrated four-component robotic inspection system across all four TLO bins over a single two-day maintenance window. Each element of the system can operate independently but deployed together they transform a traditional inspection into a structured, spatially referenced, repeatable dataset.
Zenith – high-resolution visual survey from above, without entry
The Zenith 4K vertical inspection system was deployed from the bin top aperture without any personnel entry. Its stabilised camera and integrated lighting array captured continuous, high-resolution imagery of the full internal surface in real time, from crown to floor, providing the maintenance team with a live visual feed and a permanent, reviewable record of liner condition throughout the inspection.
Snowcat-E – robotic ultrasonic thickness mapping across the full liner surface
The magnetically tracked Snowcat-E crawler was deployed into each bin to systematically capture ultrasonic thickness readings across all internal liner surfaces. Operating autonomously across vertical walls, curved sections, and floor areas, the robot maintained consistent probe contact for reliable, repeatable measurement, reaching areas no rope access technician could safely inspect.
Where surface buildup was detected, Snowcat-E’s rotary brush payload cleared the area in situ before measurement, eliminating the need for manual pre-cleaning and ensuring data accuracy across the full coverage area. All four surfaces, previously only partially inspectable, were fully mapped.
Argus 3D SLAM – the spatial backbone that ties everything together
Running in parallel with visual and UT capture, the Argus 3D SLAM system generated a high-accuracy point cloud of each bin interior. Every visual image and every UT reading was mapped to a precise spatial location within the 3D model, creating a single navigable dataset from which the maintenance team can interrogate any point in the asset, overlay measurements from different inspection cycles, and track wear progression in specific zones over time.
This is the step that converts inspection data into a predictive maintenance tool.
Juicebox – real-time control and data export
All systems were operated and monitored through the Nexxis Juicebox controller, giving the inspection team real-time visibility of crawler position, scan coverage, and emerging data during the inspection. Completed datasets were packaged for post-processing and long-term storage, establishing a spatial wear baseline for all four bins, a reference point that every future inspection can be directly compared against
Inspection Results: Safer, Faster, Fully Mapped TLO Bin Inspections
Measured results from the first deployment.
The shift from rope access to robotic inspection delivered immediate, quantifiable improvements and established the foundation for ongoing compounding value as wear-rate data accumulates across inspection cycles.
Zero personnel working at height or inside a confined space. Across the entire four-bin inspection programme, the equivalent of what previously required six to eight personnel suspended inside a confined space for up to 14 hours per bin, not a single person entered a TLO bin. All data was captured remotely.
68% reduction in inspection time per bin. Each bin was inspected in approximately four hours including setup and demobilisation, compared to 12 to 14 hours under rope access. The two-day window for all four bins returned the maintenance shutdown to production ahead of schedule.
100% liner surface coverage with spatially mapped UT data. For the first time, the maintenance team had full coverage UT readings, not spot checks from accessible areas, but a complete mapped dataset of liner thickness across every surface zone in every bin.
A critical wear finding identified in the first inspection. During the initial robotic inspection, Argus 3D SLAM and Snowcat-E data identified accelerated wear in one section of a bin liner that had not been accessible, or detected, under previous rope access inspections. Early intervention was scheduled before the area progressed to failure.
Why It Works: From Snapshot to System
The conventional case for robotic inspection focuses on safety and speed. Both are real. But the more significant shift is in what the data enables.
A rope access inspection produces a report. A Nexxis inspection produces a dataset — spatially referenced, repeatable, comparable over time, and exportable into predictive maintenance planning systems. Each inspection builds on the last. Wear rates become calculable. Remaining liner life becomes forecastable. Maintenance interventions move from reactive to predictive.
For assets as critical and as historically under-measured as TLO bins, that shift is not incremental. It changes how the asset is managed.
Contact us to find out more about our solutions to TLO bin inspection, or other products, or request a quote online.