They spoke a lot on Thursday about "process optimization" and “anomaly detection” models, and a slide in their talk presented the example of system bearings, where the failure of one could lead to the failure of more, causing downtime that can be very costly for an industrial operation.
Seeq’s solution? SMEs cleanse available data on the machine’s operation, they identify normal operation, calculate statistical thresholds—in this case the maximum stress on bearings and the point where they might fail—and identify deviations from their normal performance.
The result? Maintenance is notified of abnormal operation days before a possible failure. That's the definition of predictive maintenance.
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Seeq’s solutions aren’t one-size-fits-all, Tropsa noted. The company has preview servers available for any industry IT personnel who want to test Seeq's solutions and see how they might be adapted to fit their employers' operations.
“There is no one algorithm that will solve all problems,” he added. “This is why it’s important to have flexible analytics platforms.”
And a plant process engineer doesn't necessarily need to know complex software coding to use Seeq's solutions because they were originally designed for process engineers, who might not necessarily hold that knowledge, though taking advantage of some capabilities since the original release make coding acumen handy.
Seeq's solutions are all about manufacturers reaching their their digital transformation goals. More than 88% of companies fall short.
"Assess how your digital transformation is going," Conde advised. "Many organizations are data rich, evaluation poor."
