Moving Beyond Prediction with AI-Based Detection Models
The wind industry faces immense pressure to maximise asset availability and reduce operational expenditure. Traditional maintenance strategies, whether based on fixed schedules or simple alerts, often fall short, leading to costly unplanned downtime, unnecessary component replacements, and missed opportunities for optimisation. The challenge is moving from predictive models, which only forecast a failure, to prescriptive maintenance, which provides specific, actionable recommendations. This article provides a technical explanation of how artificial-intelligence-based detection models bridge this gap, using complex data to diagnose developing faults and prescribe a clear course of action, ultimately improving turbine reliability and reducing O&M costs.
By Silvio Rodrigues, Chief Innovation Officer and Co-founder, Jungle, Portugal
