Adaptive adjacent signal difference lasso for bearing fault detection

Abstract

Extraction of the fault-related periodic impulses from vibration signals with heavy background noise is fundamental but difficult for bearing fault diagnosis. In this paper, a novel fault feature extraction method based on a new sparse representation, adaptive adjacent signal difference lasso (AdaASDL) is proposed for bearing fault diagnosis. AdaASDL model is composed of l1-norm sparse regularization term and adjacent signal difference sparse regularization term, which are used to enhance the sparsity of the signal amplitude and adjacent signal difference, respectively. Moreover, a weighted method is proposed for setting the regularization parameter adaptively. The majorization–minimization (MM)-based optimization algorithm is derived to solve the objection function optimization of AdaASDL. In comparison with other state-of-the-art methods including the conventional sparse representation and spectral kurtosis (SK), AdaASDL has the better denoising performance on simulation signals and bearing vibration signals and the comparison results demonstrate the superiority of AdaASDL in bearing fault diagnosis.