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Home » Rapid AI Detection and Response Prevent Catastrophe at Wood Products Plant

Rapid AI Detection and Response Prevent Catastrophe at Wood Products Plant

Win of the Week

The risk of fire is top of mind for any wood products plant. Heavy dust contamination in the environment creates risk of a fire quickly spreading through the facility. In this Win-of-the-Week, learn how Augury AI prevented a near miss at a prominent wood products company.

Industry – Wood Products //
Machine – Belt Driven Fan //
Fault Type – Fire in Fan //

Image 1:
Machine Health status, alerts, and communications between Augury vibration analysts and the onsite team are tracked and logged in the Augury platform as issues are detected and addressed.

In mid-July, Augury’s AI detected and automatically alerted the customer about an anomaly — a very rapid increase in temperature and vibration — on a driven fan bearing at one of the company’s west coast plants. Augury’s vibration analyst team notified the customer as well, asking them to inspect as soon as possible. The vibration analyst then updated the Machine Health status to Danger, while Augury’s CMMS integration created an automated work order and also notified the customer. As a result, the plant maintenance staff received immediate, recommended actions to rapidly address this critical alert before it became a catastrophic issue.

Image 2:
Augury’s algorithms analyze over 840 unique feature sets. Changes in Vibration Velocity RMS, Vibration AccelerationP2P, and Temperature Sensor at time of the alert are shown below.

The onsite team found that a fire had ignited in the fan. Due to their quick response, the damage was limited to the fan and bearing. The hero, the Augury Halo Endpoint — the smart wireless sensor that enables real-time continuous monitoring of critical equipment — also took a hit during the save (below)!

Had Augury’s system not been monitoring this fan, the fire would likely have gone undetected until it had grown and spread. Augury’s rapid detection assisted in preventing catastrophic secondary damage, extensive downtime, and potential safety risks associated with the fire spreading before being identified.

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