Comparative analysis of wind turbine Gearbox fault detection using SCADA, CMS, and ISO 20816-21 vibration data: Individual vs. integrated monitoring approaches
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Wind turbine gearboxes represent critical, failure-prone components with high replacement costs and extended downtime, making effective fault detection essential for reducing operational costs and preventing catastrophic failures. Current monitoring approaches utilize SCADA operational data, dedicated CMS (Condition Monitoring Systems), and vibration analysis following ISO 20816-21 standards; however, their comparative effectiveness and integrated potential remain inadequately explored. This research presents a comprehensive comparison of gearbox fault detection capabilities using three monitoring paradigms: SCADA-based operational parameter analysis, dedicated CMS diagnostics, and ISO 20816-21 compliant vibration trend monitoring. The study evaluates individual system performance and quantifies the benefits of integrated multi-source detection strategies using real-world operational data from wind turbine fleets. The methodology implements parallel detection frameworks analyzing: (1) SCADA operational parameters including gearbox oil temperature, bearing temperatures, rotational speeds and power output; (2) CMS monitoring incorporating high-resolution vibration signals and event-based alerts from strategically positioned accelerometers; (3) ISO 20816-21 vibration analysis using broadband RMS velocity values as standardized health indices. Each dataset is independently analyzed using threshold-based techniques, statistical anomaly detection, and supervised learning models, followed by data fusion into a unified diagnostic framework. Performance evaluation considers fault detection lead time, diagnostic accuracy, fault type differentiation capability, false positive rates, and computational requirements. Individual analysis reveals SCADA provides broad operational context with detection days in advance but limited early warning capabilities. CMS delivers high sensitivity to mechanical faults with early warning capabilities but exhibits high false-positive rates and increased computational costs. ISO 20816-21 vibration analysis provides standardized, interpretable health indices aligning with maintenance practices but generates load-dependent false alarms. The integrated approach significantly outperforms individual methods, achieving faster and more accurate fault detection with enhanced diagnostic resolution, particularly in complex failure scenarios. This study demonstrates the complementary nature of these monitoring approaches and establishes a framewor