Early anomaly detection: Motor of starch dryer fan

From downtime risk to proactive maintenance

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Executive summary

With Viking Analytics’ anomaly detection, we were able to detect issues far earlier than with traditional vibration thresholds. Identifying motor looseness months ahead of failure gave us the time to plan a controlled shutdown, saving us from costly downtime and unplanned production losses.

Hector Velazquez Mtz

Reliability and vibration expert

Customer

Employees

Turnover

Location

Production

Starch drying

Heat

Electricity

A starch producer operating multiple large dryers improved its maintenance strategy by equipping a critical 600 hp dryer fan motor with triaxial wireless vibration sensors connected to Viking Analytics’ AI platform. Previously reliant on monthly route-based inspections, the team now benefits from continuous monitoring and AI-driven anomaly detection. The system flagged subtle vibration changes that revealed motor looseness long before failure, enabling a planned replacement months in advance. This proactive approach reduced downtime risk, avoided unnecessary costs, and ensured uninterrupted production across several dryers of the same type.

Solution

Viking Analytics AI + Triaxial wireless vibration sensor on DE Motor

Challenge

Maintenance of the starch dryer fan motor relied only on monthly route-based inspections. This created limited visibility between checks and increased the risk of detecting problems too late. With such a critical asset, any unexpected failure could lead to costly downtime, labor expenses, and disruptions in production.

Assets

600 hp motor (1790 RPM), SKF 6226 bearings, flow 166,950 m³/h, 4 other dryers with same motor type

Location

Early anomaly detection: Motor of starch dryer fan

Executive summary

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