Nexxis Solutions for Fixed Plant Inspection

As mining operations move toward longer-term predictive maintenance strategies, inspection data has become a critical business driver. Understanding not just the current condition of an asset—but how it changes over time—is essential to managing wear rates, planning maintenance, and reducing unplanned downtime. 

For Transfer Load Out (TLO) bins and chutes, Nexxis’ approach combines robotic inspection, quantitative measurement, and spatial data capture into a single, repeatable workflow. Rather than relying on one-off visual inspections, this methodology is designed to support accurate, repeatable, and automatable inspections across the asset lifecycle. 

 

The Asset 

TLO bins and chutes are high-wear assets within fixed plant operations, exposed to constant material flow, impact, and abrasion. Internal liners are particularly susceptible to wear and degradation, making regular inspection essential to maintaining throughput and asset integrity. 

Traditional inspection methods often require rope access and confined space entry, limiting inspection frequency and increasing safety risk—while still only providing limited quantitative data for long-term condition monitoring. 

The Challenge 

Asset owners increasingly require inspection methods that deliver more than visual confirmation. Key challenges include: 

• Capturing quantitative thickness data to measure liner wear rates 

• Accurately localising inspection data within complex internal geometries 

• Eliminating working at heights, confined space entry, and rope access 

• Creating a single, reliable dataset suitable for long-term comparison 

• Enabling repeatable and scalable inspections that support automation 

Without spatial context and repeatability, inspections remain reactive—making predictive maintenance difficult to achieve. 

 

The Approach 

Nexxis’ approach to TLO bin inspection is based on a modular inspection toolbox, allowing each element to operate independently or as part of an integrated system. 

Zenith provides remote controlled, high-resolution visual inspection surveys of internal surfaces. Its stabilised camera system and lighting enable consistent imagery in confined, low-light environments, supporting clear condition assessment without the need for personnel entry. 

Snowcat-E, selected based on liner material and inspection requirements, is used to capture ultrasonic thickness measurements from internal liners. The tracked robotic platform enables stable traversal and repeatable probe contact across bin surfaces, removing the need for rope access or confined space entry. 

Where surface condition may impact UT coupling, Snowcat-E can also be configured with a rotary brush payload to prepare inspection areas prior to measurement. This allows debris, scale, or buildup to be removed in situ, improving data quality and reducing the need for manual pre-cleaning. 

Argus 3D SLAM forms the data backbone of the inspection, generating a spatially accurate point cloud of the asset. All visual imagery and thickness readings are mapped into this 3D model, creating a single, accurate data map that improves reliability and supports post-processing, reporting, and long-term analysis. 

Outcome & Value 

By shifting from access-based inspections to data-driven workflows, this approach enables: 

• Removal of working at heights, confined space entry, and rope access 

• Accurate capture and localisation of ultrasonic thickness readings 

• Improved UT data quality through optional robotic surface preparation 

• Integration of visual, quantitative, and spatial data into a single dataset 

• Repeatable inspections that support wear-rate tracking over time 

• A scalable methodology suitable for leasing models or service-provider deployment 

• A clear pathway toward automated inspections and predictive maintenance 

Rather than producing a one-off inspection snapshot, this methodology turns each inspection into a measurable, repeatable dataset—supporting earlier intervention, more targeted maintenance, and reduced unplanned downtime. 

Enabling Predictive Maintenance 

Predictive maintenance depends on understanding how an asset evolves over time. By combining robotic inspection, quantitative measurement, surface preparation, and spatial mapping, Nexxis’ approach enables TLO bin inspections to become a core component of long-term asset integrity strategies—rather than a reactive maintenance task.