Spectral ensemble sparse representation classification approach for super-robust health diagnostics of wind turbine planetary gearbox

Abstract

Health monitoring, diagnostics and prognostics techniques have been deemed as the most promising and essential framework towards smart operation and maintenance of wind energy equipment. Wind turbine planetary gearboxes have remained the most intricate and challenging transmission units to implement intelligent health diagnostics in wind power generation systems. To resolve this issue, we present a novel spectral ensemble sparse representation classification (S-ESRC) approach for super-robust health diagnostics of wind turbine planetary gearboxes. Specifically, S-ESRC implements super-robust health diagnostics via three procedures consisting of data augmentation, spectral dictionary design, and spectral sparse approximation-based diagnostic scheme. Firstly, the prediction translation-invariance is exploited to accomplish vibrational data augmentation. Second, the spectral dictionary design with robust and strong reconstruction capability is achieved via spectrum construction and feature fusion considering the intra-class and inter-class attributes. Thirdly, the spectral sparse approximation error-based diagnostic scheme is applied to accomplish robust health diagnostics. Experimental validations using a wind turbine planetary gearbox system have demonstrated the applicability and superiority of S-ESRC for super-robust health diagnostics. Comparative studies have comprehensively shown the super-robust performances of S-ESRC including superior diagnostic accuracy, strong robustness to random noises, strong robustness to hyperparameters, and efficient computation costs in comparison with several state-of-the-art approaches.