Efficient parallel upscaling of river drainage networks for large data sets

Wind turbine gearboxes represent critical, failure-prone components with highreplacement costs and extended downtime, making effective fault detection essential forreducing operational costs and preventing catastrophic failures. Current monitoring approachesutilize supervisory control and data acquisition (SCADA) data, dedicated condition monitoringsystem (CMS), and vibration analysis following ISO 20816-21 standards, but their comparativeeffectiveness and integrated potential remain inadequately explored. This research presents acomprehensive comparison of gearbox fault detection capabilities using three monitoringparadigms: SCADA-based operational parameter analysis, dedicated CMS diagnostics, and SO 20816-21 compliant vibration trend monitoring. The study evaluates individual systemperformance and quantifies the benefits of integrated multi-source detection strategies usingreal-world operational data from wind turbine fleets. The results demonstrated that the vibration-based anomaly detection model was more effective than the SCADA methodology, with the ability to detect anomalies several months in advance.

Journal article