Metadata-Driven Engineering of Wind Turbine Logs for Condition Monitoring

Abstract

Wind turbine condition monitoring requires more than collecting SCADA values, alarms, vibration records, and maintenance notes. These records must be connected through reliable metadata so that each signal can be traced to the correct turbine asset, component, sensor type, alarm event, and maintenance action. This article presents a metadata-driven data engineering framework for wind turbine logs and condition monitoring systems. The framework registers turbine data sources, normalizes SCADA and alarm records, enriches logs with component tags, links events across time windows, constructs conditionmonitoring features, and scores predictive maintenance readiness. The results show that a unified monitoring layer improves metadata completeness, log linkage accuracy, and component tagging reliability across turbine data layers. The pipeline maturity analysis further shows that condition monitoring readiness, fault traceability, and predictive maintenance confidence improve when raw logs are transformed into metadata-enriched and component-aware monitoring records. These findings indicate that predictive wind turbine maintenance depends not only on diagnostic models but also on the quality, traceability, and component specificity of the underlying data pipeline.