Predictive maintenance for a conveyor-system network

Across a large network of industrial conveyor belts in mineral processing, sudden mechanical failures and unplanned downtime kept threatening. The maintenance managers preferred to steer via direct visual checks and simple email hints rather than navigate complex asset-management platforms.

We deployed a locally running anomaly-detection model that evaluates the raw vibration telemetry from clamp-on acoustic sensors. Instead of pushing yet another dashboard on the team, we configured the AI to send a clear, plain email straight into the maintenance manager's inbox around 48 hours before an expected failure.

Unplanned downtime fell noticeably. The model proved itself in daily use precisely because it delivers its warnings simply and actionably in the team's already-used communication channel.