Rolling bearing fault diagnosis method based on modified fourier mode decomposition and band entropy
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摘要:
目的 针对多分量、强背景噪声下滚动轴承故障特征提取困难的问题，提出一种将改进傅里叶模态分解（MFMD）和频带熵（FBE）分析相结合的滚动轴承故障特征提取方法。针对傅里叶分解（FDM）在强背景噪声下边界频率偏移和过分解等问题，提出频带熵和包络谱相结合的敏感频带和敏感模态分量选取方法。 方法 首先，通过FBE分析选取频带熵区域的极小值，将其作为敏感频带的中心频率并确定敏感频带边界；然后，在敏感频带区间内对信号进行带限傅里叶模态分解，从而获得若干个相互正交的傅里叶本征模态函数（FIMF）及其边际希尔伯特谱；其次，根据FIMFs与原信号频带熵的区域从属关系，选取可以反映故障特征的敏感FIMFs；最后，对所选取的FIMFs进行包络谱分析并提取故障特征。 结果 轴承仿真和实验结果表明，该方法可以实现轴承故障的精确诊断。 结论 研究成果可为滚动轴承的健康状态评估提供参考。 Abstract:Objective In order to resolve the difficulty of extracting the fault features of rolling bearings under conditions of multiple components and strong background noise, this paper proposes a rolling bearing fault feature extraction method based on modified Fourier mode decomposition (MFMD) and frequency band entropy (FBE) analysis. In order to solve the problem of the boundary frequency offset and over-decomposition of the Fourier decomposition method (FDM) under strong background noise, a method for selecting sensitive frequency bands and mode components based on band entropy and the envelope spectrum is proposed. Methods First, the minimum band entropy value is selected as the central frequency of the sensitive band, while the boundary of the sensitive band is determined by FBE analysis. Second, the signal is decomposed by band-limited Fourier mode decomposition in the sensitive frequency band, and several mutually orthogonal Fourier intrinsic mode functions (FIMF) and their marginal Hilbert spectra are obtained. Next, sensitive FIMFs which can reflect fault characteristics are selected according to the regional dependency relationship between the FIMFs and the FBE of the original signal. Finally, the selected FIMFs are analyzed by envelope spectrum analysis to extract the fault features. Results The accurate diagnosis of bearing faults can be realized by applying this method to bearing simulation data and experimental data. Conclusions The results prove the effectiveness and superiority of the proposed method, which can provide technical support for the health evaluation of rolling bearings. -
图 4 轴承故障仿真信号的时域波形与频谱
Figure 4. Time domain waveform and spectrum of bearing fault simulation signal
图 5 轴承故障仿真信号的时频分布
Figure 5. Time-frequency distribution of bearing fault simulation signal
图 7 原始信号不同窗长下的频带熵分析
Figure 7. Frequency band entropy analysis of original signal with different window lengths
图 11 EMD分解和包络谱的分析结果
Figure 11. Results of EMD decomposition and envelope spectrum analysis
图 13 轴承振动信号的短时傅里叶变换
Figure 13. Short time Fourier transform of bearing vibration signal
图 14 轴承振动信号的频带熵分析
Figure 14. Frequency band entropy analysis of bearing vibration signal
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