Condition monitoring of High-Speed Shaft Bearing (HSSB) in Wind Turbine Generators (WTGs) remains a challenging subject for industrial and academic studies. The investigation of mechanical vibration signals presents the most popular method in the literature. Consequently, this work involves a novel data-driven approach for direct HSSB prognosis using the vibration analysis. The proposed method is based on the computation of traditional statistical metrics derived both from the time-domain and frequency-domain via Spectral Kurtosis (SK). Then, the selection of the most suitable features was made using three metrics (monotonicity, trendability, prognosablity) to guarantee a better generalization of the trained Elman Neural Network (ENN). The validation of the proposed method was done using the benchmark of the center for Intelligent Maintenance Systems (IMS) for training and real measured Green Power Monitoring Systems (GPMS) data for testing. We have provided two links for downloading these data sets. The experimental results show that the proposed approach presents a powerful prediction tool. Comparative results with previous work show several advantages for the proposed combination of statistical metrics and ENN, such as the external prediction and real online estimation of the Remaining Useful Life (RUL). Also, some new practical findings are provided in the discussion.
Implementation of neural network-based maximum power tracking control for wind turbine generators
