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) to wind gear oil diagnosis. Initially, a stepwise multivariate regression (SMR) model is introduced and optimized after data process, which integrates a multiscale feature and AIC diagnosis feature. Subsequently, to tackle data heterogeneity among different indicators, canonical correlation series of representations are extracted from the SMR models, and a combining 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 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.