EKBD-MK: Entropy-Kurtosis bilateral discernment with maximum kurtosis blind deconvolution for fault diagnosis in wind turbine systems

Wind turbines (WTs), as critical components of renewable energy systems, face significant challenges in maintaining operational reliability due to their bearing systems operating under challenging mechanical and environmental conditions. These conditions are further complicated by transient events generating sudden mechanical impulses and persistent harmonic noise from mechanical resonance, which obscure early indicators of component degradation and hinder fault detection. To address these challenges, this research proposes EKBD-MK (Entropy-Kurtosis bilateral discernment with maximum kurtosis blind deconvolution), a novel diagnostic framework that systematically suppresses non-stationary transient disturbances and irrelevant harmonic components in vibration signals. By employing a Shannon entropy-based noise quantification approach alongside physical interpretations of center frequencies, the proposed method ensures robustness in complex noise environments. Additionally, a signal decomposition strategy through Variational Mode Decomposition (VMD) is developed with an entropy-frequency dual-constraint system to optimize fault information concentration, prevent feature loss, and maintain signal integrity. To further enhance diagnostic accuracy, an adaptive blind deconvolution strategy incorporating multi-component preservation and spectral kurtosis-based enhancement of fault features is implemented, ensuring parameter insensitivity and effective noise suppression. Experimental validation through two case studies demonstrates the efficacy of this framework, significantly improving diagnostic precision compared to current methods.