Scaling Lessons Learned Across Wind Fleets at Time of Need Using Generative AI and Maintenance History

In modern wind fleets, predictive analytics routinely raise early-warning alerts but the real challenge is turning those alerts into informed action at the moment decisions need to be made. In this talk, I will present how Generative AI can unlock the lessons learned buried in years of handwritten and unstructured maintenance history and make that knowledge instantly available to operators across multiple wind farms, turbine brands, and models.
The session will walk through a real-world implementation where maintenance logs, downtime records, and failure histories from diverse sites were standardized and organized into a searchable, structured knowledge base using Large Language Models. Building on this foundation, we developed a decision-support layer that automatically links each new early-warning flag to historically similar cases from the fleet. When an alert appears, operators are not only notified of the anomaly, but they also receive contextualized insights such as typical root causes, prior maintenance interventions, expected downtime ranges, and estimated production loss, all generated at the time of need inside the early-warning interface.

The presentation will highlight three main sources of operational value. First, it reduces uncertainty and accelerates response by transforming alerts from “what is happening?” to “what has happened before, and what worked?” Second, it scales institutional learning across sites, enabling a lesson learned at one wind farm to benefit every other site in the portfolio. Third, it mitigates the risk of knowledge loss when key personnel rotate, retire, or leave, by capturing their experience in a reusable, AI-curated knowledge system.

Rather than positioning AI as a replacement for expertise, the talk frames Generative AI as a practical operations companion; one that helps teams prioritize actions, plan maintenance more effectively, and respond more consistently across diverse turbine fleets. The session will conclude with field deployment results, lessons learned from implementation, and practical guidance for organizations interested in operationalizing their own maintenance history using AI-driven contextual decision support.