自适应奇异值分解的随机共振提取微弱故障特征

针对农业机械设备在强背景噪声下微弱故障特征难以提取的问题,提出一种基于自适应奇异值分解的随机共振微弱故障特征提取方法。首先,将原始信号奇异值分解并重构得到分量信号,构建互信息差分谱,权衡各分量信号对原始信号的贡献率,自适应选取有效奇异值个数,以克服已有方法人为主观选择或仅考虑奇异值大小等不足;其次,对选取的有效奇异值对应的分量信号自适应随机共振,使其微弱故障特征增强;最后,对增强的分量信号统计学平均以提取微弱故障特征。仿真和轴承外圈故障试验结果表明,该方法不仅克服了强背景噪声下有效奇异值的选取困难,而且结合自适应随机共振,有效提取出仿真信号100 Hz和轴承外圈155.5 Hz的故障特征频率,因此,所提方法不仅能够更好的增强微弱故障特征,而且分析结果优于单纯的奇异值分解和随机共振方法。该文提出的方法不仅可适用于强噪声背景下轴承的故障诊断,同时为农业机械设备的轴承故障诊断提供参考。

Extracting weak fault characteristics with adaptive singular value decomposition and stochastic resonance

Abstract: Bearings are the important component of agricultural machinery and equipment, whose failure may seriously endanger the healthy operation of equipment and even lead to bodily injury. So the fault diagnosis of agricultural machinery and equipment gains more and more attention. Using the vibration signal to extract the fault characteristics is the most common method, but it is difficult to extract the weak fault characteristics in strong background noise. Therefore, the extraction of weak fault characteristics with very low SNR (signal-to-noise ratio) under strong background noise becomes the key to the fault diagnosis of agricultural machinery bearings. There are 2 general methods for weak feature extraction under weak background noise. One method is to extract weak faults from the perspective of suppressing or eliminating noise. The other one is not to eliminate noise but using noise to improve the SNR to extract the weak fault characteristics, such as stochastic resonance (SR) theory. Compared to the traditional noise reduction method, SR makes use of noise energy transfer to weak signal, so the weak fault characteristics are enhanced while some of the noises are weakened. Because of the excellent features of extracting weak fault characteristics in strong background noise, SR has become a hot topic for many scholars in recent years. In this paper, the weak fault characteristics extraction method of SR based on adaptive SVD (singular value decomposition) is proposed. In the method, firstly, the original signal is decomposed by singular value and reconstructed to obtain the component signal; the difference spectrum of mutual information is constructed, the mutual information of each component signal and the original signal is weighed, and the number of valid singular values is selected adaptively, in order to overcome the problem of existing methods including human subjective choice or only considering the size of singular values and other deficiencies. Using the mutual information difference spectrum, 3 and 10 effective singular values are obtained in the simulation signal and bearing outer ring signal, respectively. Secondly, the adaptive SR is performed for the component signal corresponding to the selected effective singular value which enhances weak fault characteristics. Finally, the enhanced component signals are statistically averaged to extract the weak fault characteristics. In this paper, constructing the mutual information differential spectrum, and considering the mutual information of the component signal and the original signal, on the one hand, it avoids the elimination of the useful signals; on the other hand, the adaptive selection is realized which avoids the subjectivity of the artificial selection. In addition, due to the presence of strong background noise, the larger singular value may have smaller mutual information, but it is not valid singular value. It indicates that large singular value does not necessarily contain useful information, and there may be noise interference. Hence, the selection of effective singular values should not be based on the size of the singular value. The above analysis shows that it is difficult to extract the weak fault characteristics by SVD in strong background noise. We combine the 2 methods to process the effective component signal selected by mutual information difference spectrum in SR, and the maximum spectral frequency of each component is obtained. The statistical average is used to achieve noise filtering in order to highlight the characteristics of weak fault frequency. The results of simulation and bearing outer ring test show that, the proposed method is superior to the SVD and SR method. The method can effectively extract 100 and 155.5 Hz weak fault characteristics respectively for simulation signal and bearing outer ring signal. The proposed method can be applied not only to the fault diagnosis of bearing in strong noise background, but also to provide reference for bearing fault diagnosis of agricultural machinery and equipment.