A Simultaneous Impulse and Oscillatory Component Decomposition Method for Fault Diagnosis of Rolling Bearings

Abstract

Due to the interference of oscillatory components and noise, the periodic impulses associated with localized bearing faults become difficult to extract, leading to unreliable diagnostic performance. To solve this problem, the study proposes a simultaneous impulse and oscillatory component decomposition method (SIOCD). The method designs and solves a novel optimization model to decompose oscillatory components and fault impulse components from noisy vibration signals. To achieve component separation in the optimization model, distinct penalty functions are introduced for oscillatory and impulse components. For oscillatory components, a regularization term is applied to achieve their extraction by minimizing the component bandwidth in the frequency domain. For impulse components, a penalty function is designed to achieve their decomposition by enhancing both sparsity within groups (SWG) and sparsity across groups (SAG) in the time domain. Then, an iterative solver is derived using an alternating minimization framework and the majorization-minimization (MM) algorithm. Finally, the proposed method’s effectiveness is demonstrated through comprehensive simulation and experimental analyses, and the results demonstrate that it achieves superior performance over existing approaches in fault feature extraction and enhancement.