Enhancing Maintenance & Post-Deployment Checks for Mining and Heavy Industries
Overview
Post-deployment equipment health checks are critical in mining and heavy industry environments where extreme weather, vibration, and continuous operation lead to accelerated wear and tear. Traditional maintenance routines often rely on manual inspections, which are time-consuming, inconsistent, and reactive rather than preventative.
NorrSpect, based in Umeå, Sweden, is a pioneer in delivering AI-based inspection systems for industrial leaders including Volvo Cars. With NorrStudio, mining and heavy equipment operators now gain the ability to perform automated, AI-driven maintenance checks—ensuring operational safety, performance, and uptime.
Maintenance & Field Inspection Challenges
Mining and heavy industrial operations commonly face:
Rust accumulation on chassis and structural components, especially in humid or corrosive environments.
Conveyor belt misalignment, increasing mechanical stress and reducing system life.
Cracks in rotating blades or fans, posing catastrophic risk if undetected.
Oil leakage from gearboxes or hydraulic systems, reducing performance and causing safety hazards.
Faded or obscured safety decals, leading to compliance issues or operator misuse.
Cracked operator screens, making controls unreadable and risking incorrect operation.
Manual checks are prone to oversight, especially under time pressure or in hazardous zones.
NorrStudio: AI for Proactive Equipment Inspection
NorrStudio brings industrial-grade computer vision and AI to field-level inspections. Using fixed or mobile imaging setups—handheld, drone-mounted, or robot-integrated—the system continuously monitors asset conditions and flags early warning signs of degradation or risk.
Targeted AI Capabilities
Rust Detection on Chassis and Frames
Deep learning models detect and localize corrosion on painted or bare metal, enabling early intervention before structural compromise.
Conveyor Belt Misalignment Alerts
Real-time visual feedback monitors belt positioning, edge fray, or drift, helping avoid unplanned shutdowns.
Crack Identification on Rotating Elements
AI models analyze fan blades, impellers, or rotors for hairline cracks—detecting fatigue before mechanical failure occurs.
Oil Leak Spotting on Gears and Seals
The system identifies fluid stains, drips, or oil pooling around gearboxes or hydraulic interfaces, even when visibility is limited.
Safety Decal Visibility Scanning
Ensures warning, hazard, and instruction labels are present and readable on all equipment surfaces per regulatory standards.
Touchscreen or Display Damage Detection
Inspects operator interface panels for screen damage, obscured readouts, or cracks that could affect usability and safety.
Field Results and Impact
Metric | Before NorrStudio | After NorrStudio |
|---|---|---|
Rust-related frame failures | Occasional | Prevented via early detection |
Conveyor misalignment shutdowns | Several per year | Reduced by 90% |
Unreported blade cracks | Detected late | Flagged early and proactively repaired |
Oil leak undetected incidents | Frequent | Now caught within hours |
Safety decal compliance | Manual tracking | Fully automated, up to 98% accuracy |
Downtime from screen-related issues | Reactive | Preemptive servicing enabled |
About NorrSpect
Founded in Umeå, Sweden, NorrSpect is a leader in practical AI systems for production and industrial environments. From precision automotive lines at Volvo Cars to remote mining sites, NorrSpect builds AI tools that thrive in rugged, high-variability settings.
NorrStudio supports flexible deployment options—from stationary cameras on fixed equipment to mobile or drone-based visual scanning—and integrates seamlessly with maintenance logs and ERP systems.
