Rolling bearing fault feature detection using nonconvex wavelet total variation

• Published in: Measurement : journal of the International Measurement Confederation 2021-07, Vol.179, p.109471, Article 109471
• Main Authors: Wang, Kaibo, Jiang, Hongkai, Hai, Bin, Yao, Renhe
• Format: Article
• Language: English
• Subjects: Bearings; Convex optimization; Convexity; Cost analysis; Cost function; Fault feature detection; Iterative algorithms; Iterative methods; Kurtosis; Mathematical models; Minmax concave penalty; Model accuracy; Noise measurement; Nonconvex wavelet total variation; Parameters; Regularization; Roller bearings; Rolling bearng; Rotating machinery; Studies; Vibration; Vibration measurement
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2021.109471

•Nonconvex WATV model is constructed by using MC function.•Convexity of the constructed cost function is guaranteed.•Derive an iterative algorithm to solve the constructed convex optimal problem.•Give a reference for hyper parameter selection.•Detailed simulation and experiments validate the effectiveness of nonconvex WATV. 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.