Nonconvex Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis

Abstract

Vibration monitoring is one of the most effective ways for bearing fault diagnosis, and a challenge is how to accurately estimate bearing fault signals from noisy vibration signals. In this paper, a nonconvex sparse regularization method for bearing fault diagnosis is proposed based on the generalized minimax-concave (GMC) penalty, which maintains the convexity of the sparsity-regularized least squares cost function, and thus the global minimum can be solved by convex optimization algorithms. Furthermore, we introduce a k-sparsity strategy for the adaptive selection of the regularization parameter. The main advantage over conventional filtering methods is that GMC can better preserve the bearing fault signal while reducing the interference of noise and other components; thus, it can significantly improve the estimation accuracy of the bearing fault signal. A simulation study and two run-to-failure experiments verify the effectiveness of GMC in the diagnosis of localized faults in rolling bearings, and the comparison studies show that GMC provides more accurate estimation results than L1-norm regularization and spectral kurtosis.