Zhang Xin, Liu Zhiwen, Wang Lei, Zhang Jiantao, Han Wei
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China.
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China.
ISA Trans. 2020 Nov;106:355-366. doi: 10.1016/j.isatra.2020.07.004. Epub 2020 Jul 3.
To accurately extract fault signatures from noisy signals, an improved orthogonal matching pursuit (OMP) with adaptive Gabor sub-dictionaries is proposed in this paper. Firstly, based on the optimal time-frequency characteristics of Gabor atom, the Gabor sub-dictionaries that adaptively change with the residual signals and have low redundancy are designed for signal sparse representations. Then, an improved OMP is developed, in which the selection of each optimal atom only needs to calculate a small number of cross-correlation operations further calculated quickly by the fast Fourier transform. Simulation study and comparisons showed that the method significantly improved the efficiency of signal sparse representations while ensuring the accuracy. Case studies and comparisons with the state-of-art methods demonstrated the effectivity of the method to extract bearing fault signatures.
为了从噪声信号中准确提取故障特征，本文提出了一种基于自适应Gabor子字典的改进正交匹配追踪（OMP）算法。首先，基于Gabor原子的最优时频特性，设计了随残差信号自适应变化且冗余度低的Gabor子字典用于信号稀疏表示。然后，开发了一种改进的OMP算法，其中每个最优原子的选择只需要计算少量互相关运算，并通过快速傅里叶变换进一步快速计算。仿真研究和比较表明，该方法在保证准确性的同时显著提高了信号稀疏表示的效率。案例研究和与现有方法的比较证明了该方法提取轴承故障特征的有效性。
