Meng Zong, Shi Ying, Chen Zijun, Pan Zuozhou, Li Jing
Department of Instrument Science and Technology, Yanshan University, Qinhuangdao 066004, China.
J Acoust Soc Am. 2019 Oct;146(4):2385. doi: 10.1121/1.5128327.
In the process of block compressed sensing (CS) applied to the rolling bearing fault signal, the reconstruction accuracy of the signal is low due to the large difference in sparsity between blocks and the unreasonable components of reconstruction support set, which affects the overall reconstruction effect of the signal. To improve the signal reconstruction results, forward and backward stagewise orthogonal matching pursuit (FBStOMP) based on the adaptive block method is proposed. First, to equalize the sparsity of each block signal, the fault signal is divided into blocks according to the adaptive block length, which is obtained by the short-time autocorrelation algorithm. Then, the K-singular value decomposition algorithm is used to train the sparse dictionary to obtain a better sparse effect. Finally, the FBStOMP algorithm is proposed. The atom backtracking and screening process is added in the reconstruction process to improve the possibility that all the effective atoms can be selected into the support set. The experimental analysis of the simulation signal and bearing fault signal show that, compared with the traditional CS reconstruction algorithm, the adaptive block-FBStOMP algorithm proposed in the paper can effectively improve the reconstruction accuracy of the bearing fault signal.
在将块压缩感知（CS）应用于滚动轴承故障信号的过程中，由于块间稀疏性差异较大以及重构支撑集成分不合理，导致信号重构精度较低，影响了信号的整体重构效果。为了提高信号重构结果，提出了基于自适应块方法的前向和后向逐段正交匹配追踪（FBStOMP）算法。首先，为均衡各块信号的稀疏性，利用短时自相关算法得到自适应块长度，据此将故障信号进行分块。然后，采用K-奇异值分解算法训练稀疏字典以获得更好的稀疏效果。最后，提出FBStOMP算法，在重构过程中增加原子回溯和筛选过程，以提高所有有效原子被选入支撑集的可能性。对仿真信号和轴承故障信号的实验分析表明，与传统CS重构算法相比，本文提出的自适应块-FBStOMP算法能有效提高轴承故障信号的重构精度。
