A novel blind deconvolution based on sparse subspace recoding for condition monitoring of wind turbine gearbox

Blind deconvolution (BD) methods have proven to be effective tools for condition monitoring of gearbox. Nevertheless, due to the severe operating environment and complex structure in the wind turbine (WT) gearbox, the prior knowledge of fault period is hard to obtain accurately, which results in a challenge to the traditional BD algorithms that exceedingly relies on this information. Motivated by this limitation, a novel BD approach based on sparse subspace recoding (SSRBD) is proposed for the condition monitoring of WT gearbox. In this work, singular value decomposition is initially introduced to convert the raw signal from the input space to feature subspaces. The coefficient of variation is then constructed to guide the choice of inverse filter length. Successively, an iterative Mahalanobis distance is designed to cluster the sensitive subspace with rich fault information. Finally, build upon the robust principal component analysis, the objective features are further separated by means of sparse recoding. The effectiveness and robustness of the proposed SSRBD are validated through several comparative analyses and experimental cases. The consequences demonstrate that the proposed approach overcomes the dependence of prior information and domain knowledge, while extracts the fault feature more effectively than the state-of-the-art methods.