Bearing Fault Detection Via B-spline Constructed Sparse Method

Abstract

This article proposes a novel period detection approach named B-spline constructed method for bearing fault detection by fully utilizing the sparsity of vibration signals. A newly constructed separated sparse representation model is proposed for vibration signals to reduce interference generated by strong background noises. In the model implementation, a B-spline dictionary is adopted to represent sparse transients due to its inherent ability to model sparsity and its high flexibility, and then, the sparse transients are estimated through split augmented Lagrangian shrinkage algorithm. A power function is proposed as a criterion for period detection based on the estimated transients. It is proven that its maximum value will be reached when the separation time parameter coincides with the theoretical period, and thus, the power function can be used to detect the fault characteristic period. The effectiveness of the proposed method is verified by both the simulated and practical vibration signals from faulty rolling bearings.