Rolling bearing fault feature detection using nonconvex wavelet total variation

Vibration signals measured from rolling bearing are often used to judge operational condition of rotation machinery. This paper proposes a nonconvex wavelet total variation method to detect rolling bearing fault feature submerged in noise measurement. Firstly, the parametric minmax concave function is used to construct a novel wavelet total variation model to improve the accuracy of signal estimation and induce more strongly sparsity. Second, convexity parameters and regularization parameters are limited in a given region to make sure convexity of the constructed cost function. With this, an iterative algorithm with guaranteed convergence is derived to efficiently obtain the global minimum of the constructed cost function. Simulation analysis and actual application validation show that the proposed method has a good impact estimation performance and impact recoverd by the proposed method preserves more accurate amplitude than that of traditional l₁-norm regularized wavelet total variation and Spectral Kurtosis.