Sparse representation based on the matching pursuit (MP) algorithm is an effective method for fault feature extraction involving rolling element bearings. However, in the sparse decomposition stage, the MP algorithm is extremely susceptible to both noise in the residual signal and excessive iterations selecting some atoms that are not related to the fault impulses, thus causing interference components in the reconstructed signal. Considering these problems, a secondary selection-based orthogonal MP (SS-OMP) algorithm is proposed in this paper. In the proposed method, all the atoms selected to decompose the residual signal are selected for a second time, and only those atoms directed by the fault impulses are retained. After the decomposition of the residual signal, these retained atoms are used to redecompose the vibration signal, and then the fault impulses in the vibration signal are extracted. The superiority of the proposed method is verified by a numerical simulation study and experimental analysis.

基于匹配追踪（MP）算法的稀疏表示是滚动轴承故障特征提取的有效方法。但是，在稀疏分解阶段，MP算法极易受到残留信号中的噪声和过度迭代的影响，这些迭代会选择一些与故障脉冲无关的原子，从而在重构信号中产生干扰分量。考虑到这些问题，提出了一种基于二次选择的正交MP（SS-OMP）算法。在提出的方法中，第二次选择了所有用于分解残留信号的原子，只保留了由故障脉冲引导的那些原子。残留信号分解后，这些保留的原子用于重新分解振动信号，然后提取振动信号中的故障脉冲。通过数值模拟和实验分析验证了该方法的优越性。