Nexxis Solutions for Mobile Plant Inspection
As mining operations move toward predictive maintenance and life-of-asset planning, understanding how mobile plant assets wear over time has become a critical priority. Visual inspections alone provide limited insight into material loss, making it difficult to quantify wear rates or plan maintenance with confidence.
Nexxis applies a data-driven inspection methodology to mobile plant assets—combining robotic access, ultrasonic thickness measurement, and 3D spatial mapping to transform dump truck tray inspections from subjective checks into repeatable, measurable datasets.
The Asset
Dump trucks and diggers operate in highly abrasive environments. Dump truck trays are exposed to constant impact, sliding abrasion, and material buildup, resulting in uneven wear across the tray floor, walls, and high-impact zones.
These assets are critical to production, yet traditional inspections often rely on visual checks or limited spot measurements, offering little insight into how wear progresses over time as well as working at heights.
The Challenge
Mobile plant inspections must overcome several challenges:
- Quantifying tray wear thickness across large surface areas
- Identifying high-wear zones not obvious from visual inspection
- Repeating measurements at the same locations over time
- Reducing reliance on manual access to elevated or hazardous areas
- Converting inspection data into actionable maintenance insight
Without spatial context and repeatability, inspections remain reactive—often identifying issues only after significant material loss has occurred.
The Approach
Nexxis’ approach to mobile plant inspection focuses on measuring wear, not just observing it.
Snowcat-E is deployed inside the dump truck tray to traverse wear surfaces in a controlled and repeatable manner. The crawler is equipped with a gimbal-mounted ultrasonic thickness (UT) probe, allowing consistent probe contact across uneven or curved tray surfaces.
Where surface condition may impact UT coupling, Snowcat-E can also be fitted with a rotary brush payload to clean inspection spots prior to measurement. This enables debris, scale, or buildup to be removed in situ, improving data quality and reducing the need for manual surface preparation.
To provide spatial context, Argus 3D SLAM is used to map the inspection area. Each thickness reading is linked to a precise location within the tray, creating a wear map that can be visualised, compared, and analysed over time.
Outcome & Value
By applying a structured, data-driven inspection methodology, this approach enables:
- Accurate mapping of wear across dump truck trays
- Quantitative thickness data captured via a gimbal-mounted UT probe
- Improved UT reliability through optional robotic surface cleaning
- Identification of high-wear and impact zones
- Repeatable inspections using consistent measurement locations
- Improved maintenance planning and tray life forecasting
- Reduced risk of unexpected failures and unplanned downtime
Each inspection becomes a measurable dataset rather than a one-off snapshot—supporting earlier intervention and more targeted maintenance decisions.
Supporting Predictive Maintenance
Predictive maintenance depends on understanding how wear develops over time. By combining Snowcat-E robotic access, ultrasonic thickness measurement, surface preparation, and spatial mapping, Nexxis’ mobile plant inspection approach enables operators to track degradation trends, plan repairs earlier, and extend the service life of critical mobile assets.
