A novel diagnosis methodology of gear oil for wind turbine combining Stepwise multivariate regression and clustered federated learning framework.

Data-driven approaches demonstrate significant potential in accurately diagnosing faults in wind turbines. To enhance diagnostic performance, we introduce a clustered federated learning framework (CFLF) for wind gear oil diagnosis. Initially, a stepwise multivariate regression (SMR) model is introduced and optimized after data processing, which integrates multiscale features and an AIC-diagnosis feature. Subsequently, to tackle data heterogeneity among different indicators, a series of canonical correlation representations are extracted from the SMR models, and a combined model of CFLF method and SMR is proposed to assess the performance of gear oil. Actual data analysis of wind turbine gear oil showcase the superior performance of the proposed model over the single SMR model with higher prediction accuracy of 35.73%. This study provides a new technique for evaluating gear oil in the wind energy sector.