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Beyond the alert: operationalizing human judgment with AI

Published in: Wind, Exclusive Articles, Industrial Insight

Beyond the alert: operationalizing human judgment with AI image

Wind operators have more asset data than ever, but turning it into a maintenance plan still depends on engineering judgment and knowledge scattered across multiple systems.

A new approach combining connected data, established engineering methods and AI could help preserve that expertise and put it to work across an entire fleet.

A condition-monitoring alert is useful, but it is not yet a maintenance plan.

Imagine an operator with several gearboxes beginning to show signs of degradation. None has failed. Some may continue operating for years; others could become expensive events if the warning signs are misunderstood or acted on too late.

The question is not simply which gearbox looks worse today. It is which components are most likely to require intervention in the next six to eighteen months, what the consequence of waiting might be and how that work should be planned across the portfolio.

Answering that question involves far more than a condition monitoring system (CMS) screen. A drivetrain engineer may review diagnostic outputs alongside supervisory control and data acquisition (SCADA) and performance data, work orders, component history, inspection findings, parts availability, crane logistics and the cost of similar work.

The asset manager then has to translate the technical picture into a campaign, a budget and a decision about risk.

Now imagine asking the fleet a broader question: which gearboxes should we be planning to touch in the next six to eighteen months, and why?

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