奇异谱分解联合互信息的主轴轴承故障特征提取研究

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (15) : 23-30.

论文

王振亚1,2，伍星 2,3，刘韬1,2，缪护4

作者信息 +

Fault feature extraction of spindle bearing based on SSD and MI

WANG Zhenya1,2, WU Xing2,3, LIU Tao1,2, MIAO Hu4

Author information +

文章历史 +

摘要

奇异谱分解算法（Singular spectrum decomposition, SSD）存在信号信噪比较低时降噪能力较差以及敏感分量个数难以确定的问题。针对上述问题，提出了SSD联合互信息理论（Mutual Information, MI）的主轴故障特征提取方法。首先，将轴承振动信号经过SSD分解，得到多个奇异谱分量（Singular spectral component，SSC）；然后分别计算原始信号与SSC之间的互信息，选择最小互信息（Minimum mutual information, MinMI）处的分量为最佳分量。由于背景噪声的影响，选取的最佳分量故障特征频率并不明显。因此，基于振动信号特点与互信息理论提出了差分突变互信息（Differential mutation mutual information, DMMI）的有效分量保留方法，通过对计算相邻SSC之和之间的MI值，保留突变点内的分量作为敏感信号，在此基础上再依据MinMI原则设计带通滤波器，对敏感信号带通滤波并进行包络解调以提取故障特征频率。通过仿真信号与真实主轴轴承数据分析表明：对信号进行DMMI保留敏感分量，结合MinMI准则的自适应滤波处理在主轴轴承故障特征提取方面表现了优异的性能。

Abstract

Singular spectrum decomposition (SSD) algorithm has the problems of poor noise reduction ability when the signal-to-noise ratio is low and the number of sensitive components is difficult to determine. Aiming at the above problems, a spindle fault feature extraction method based on SSD Mutual Information (MI) is proposed. Firstly, the bearing vibration signal is decomposed by SSD to obtain multiple singular spectral components (SSCs); Then calculate the mutual information between the original signal and SSC respectively, and select the component at the minimum mutual information (MinMI) as the best component. Due to the influence of background noise, the selected optimal component fault characteristic frequency is not obvious. Therefore, based on the characteristics of vibration signals and mutual information theory, a method to retain the effective components of differential mutation mutual information (DMMI) is proposed. By calculating the MI value between the sum of adjacent SSCs, the components in the mutation point are retained as sensitive signals. On this basis, a band-pass filter is designed according to the MinMI principle to filter the sensitive signal band-pass and envelope demodulation to extract the fault characteristic frequency. The analysis of the simulation signal and real spindle-bearing data shows that DMMI preserving sensitive components of the signal combined with MinMI adaptive filtering processing has an excellent performance in the spindle-bearing fault feature extraction.

导出引用

王振亚1,2，伍星 2,3，刘韬1,2，缪护4. 奇异谱分解联合互信息的主轴轴承故障特征提取研究[J]. 振动与冲击, 2023, 42(15): 23-30

WANG Zhenya1,2, WU Xing2,3, LIU Tao1,2, MIAO Hu4. Fault feature extraction of spindle bearing based on SSD and MI[J]. Journal of Vibration and Shock, 2023, 42(15): 23-30

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