Subtle anomalies flagged bearing damage before it escalated.
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The AI detected a bearing fault long before traditional alarms would have triggered. This gave us the opportunity to plan the replacement on our terms, avoid unexpected stoppages, and protect such a critical part of our process.
Starch drying
A starch producer improved the reliability of its critical drying process by equipping a large centrifuge with triaxial wireless vibration sensors connected to Viking Analytics’ AI platform. Traditionally dependent on monthly route-based inspections, the team now benefits from continuous anomaly detection and prioritization insights. The system flagged a bearing defect in the centrifuge basket side, even before vibration levels increased. With this early warning, the maintenance team could plan the 36-hour bearing replacement proactively, preventing extended downtime and costly production losses.
Triaxial wireless sensor with Viking Analytics machine learning algorithms
The centrifuge is a critical part of the starch drying process. With only monthly inspections, failures could develop unnoticed, leading to sudden breakdowns. Replacing the main bearing is a labor-intensive 36-hour task that must be carefully scheduled. Without early detection, a failure could result in weeks of downtime before production could resume.
Equipment: Large starch dryer centrifuge Basket diameter: 2,100 mm Rotational speed: 713 RPM Main bearing: SKF 24048CCK, basket side Monitoring: Route-based inspections once per month
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MultiViz - AI powered vibration analysis-conditioning monitoring for smarter predictive maintenance.