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.<\/p>\n
Traditional inspection methods present several limitations:<\/p>\n
Operations required a solution capable of systematically inspecting large distances, generating objective data on asset condition, and supporting maintenance decisions with field-based evidence.<\/p>\n
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.<\/p>\n
The solution integrates:<\/p>\n
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.<\/p>\n
To date, this solution has enabled, in one of our operations:<\/p>\n
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.<\/p>\n
Beyond replacing manual inspections, the system transforms inspection into a traceable, repeatable, and data-driven process. This enables:<\/p>\n
With this approach, Godelius integrates robotics and artificial intelligence to turn field data into actionable operational decisions.<\/p>\n\n\t\t