Wind Turbine Predictive Maintenance Prototype

Challenges

Unplanned downtime on a wind fleet is expensive and hard to anticipate: abnormal equipment behavior is visible in the data long before it stops a machine, but not to a person watching dashboards.
Maintenance teams need the probable cause as well as the alert, otherwise the warning does not shorten the intervention.

Approach

An AI-powered predictive maintenance prototype detects abnormal equipment behavior and alerts operators before a potential shutdown.

It helps maintenance teams reduce unplanned downtime, identify likely root causes, and take preventive action, drawing on real-time equipment data, maintenance guidelines and the history of past breakdowns.

Key features

Real-time Equipment Health Index for monitoring turbine condition
Early detection of abnormal equipment behavior
Root cause identification for potential failures
Preventive action recommendations for operators
LLM-based action insights drawing on maintenance guidelines and historical breakdowns

Impact

Up to 50%

Reduction of shutdowns — fully avoided, or an early alert before an inevitable one

Faults caught before the machine stops

As published on the current site

Root cause identification for potential failures