Predictive maintenance through video analytics

A mining operator validated computer vision to monitor critical equipment wear, improving safety, reliability, and operational insight.

Availability

Asset uptime

Safety

Risk reduced

Wear visibility

Seeing maintenance risks before they escalate

The client sought to address operational losses and safety challenges in a key smelting asset.

Objectives

Detect equipment wear early and assess feasibility of AI-driven maintenance alerts.

Opportunity

Critical smelting equipment failures generate safety risks, losses, and environmental impact.

Driving Complex Global Transformations

Proven delivery overcoming complex multi-country business challenges

Key Challenges

Manual inspections were risky and inconsistent

Limited insight into wear progression over time

High operational impact from unplanned failures

Solution

Executed a PoC using computer vision and AI

Modeled thermal insulation variability and internal temperature behavior

Evaluated feasibility of real-time alerts and operational applications

Demonstrated applicability of video analytics to maintenance scenarios

Impact

Safer and smarter maintenance

Validated AI feasibility for monitoring critical assets.

Improved understanding of wear and thermal behavior.

Reduced inspection risk for personnel.

Opened the path for predictive maintenance applications.

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