Mining operations rely on extensive conveyor belt networks to maintain continuous production. The reliability of these assets is critical, yet their inspection remains a challenge due to long distances, exposure to dust, vibration, temperature variations, and constant mechanical stress.

The Challenge

Traditional inspection methods present several limitations:

  • Partial coverage across large and distributed assets
  • Exposure of personnel to high-risk areas
  • Difficulty detecting early-stage failures through visual inspection alone
  • Limited traceability and lack of standardization across inspection campaigns
  • Risk of undetected anomalies evolving into high-impact failures

Operations required a solution capable of systematically inspecting large distances, generating objective data on asset condition, and supporting maintenance decisions with field-based evidence.

The Solution

Godelius developed an autonomous conveyor belt inspection system that combines robotics, advanced sensing technologies, and artificial intelligence to perform scheduled robotic inspections under real operating conditions.

The solution integrates:

  • Robotic platforms designed for both underground and surface mining environments
  • Multimodal data acquisition using visual, thermal, and acoustic sensors
  • AI-based analytics to detect early visible, thermal, and mechanical anomalies
  • Automated data processing and reporting to ensure traceability and prioritization of findings

The system can be adapted to different operational strategies, ranging from weekly inspections to multiple inspections per day, depending on process criticality and client requirements.

Scope and Results

To date, this solution has enabled, in one of our operations:

  • Over 1,000 km of conveyor belts inspected
  • More than 16 conveyors assessed across operational campaigns
  • Over 3,400 anomalies detected
  • More than 900 findings classified as critical
  • Over 95% average weekly coverage

The combination of visual, thermal, and acoustic analysis allows for the detection of multiple types of events, including out-of-spec components, rollers with abnormal thermal behaviour, and early signs of mechanical failure. This improves inspection coverage, increases repeatability, and reduces personnel exposure to hazardous environments.

Operational Impact

Beyond replacing manual inspections, the system transforms inspection into a traceable, repeatable, and data-driven process. This enables:

  • Improved maintenance prioritization
  • Earlier identification of risk conditions
  • Standardization across inspection campaigns
  • Strengthened operational continuity for critical assets

With this approach, Godelius integrates robotics and artificial intelligence to turn field data into actionable operational decisions.