| 尹 鹏.改进的 FEWT 及其在滚动轴承故障诊断中的应用[J].电子测量与仪器学报,2020,34(5):181-189 |
| 改进的 FEWT 及其在滚动轴承故障诊断中的应用 |
| Modified fast empirical wavelet transform and its application in fault diagnosis of rolling bearings |
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| DOI： |
| 中文关键词: 滚动轴承 趋势谱 经验小波变换 分量筛选 故障特征提取 |
| 英文关键词:rolling bearings trend spectrum empirical wavelet transform component selection fault feature extraction |
| 基金项目:云南省教育厅基金(2017ZZX148)资助项目 |
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| 中文摘要: |
| 针对快速经验小波变换(FEWT)中使用软阈值函数造成的频谱划分不合理的问题,提出了一种基于折中阈值函数的改
进的快速经验小波变换(MFEWT)方法。 方法首先通过傅里叶变换及反变换计算信号频谱的趋势谱,使用小波折中阈值函数去
噪方法对趋势谱进行优化;然后根据优化后的趋势谱建立滤波带,融合峭度准则和相关系数分量选取原则,完成 EWT 经验模态
分量的重构和特征分量的筛选,并对重构信号进行最小熵解卷积,进而计算频谱特征频率;最后,通过理论特征频率的匹配,完
成滚动轴承的故障诊断。 实验结果表明,与 FEWT 相比较,改进的快速经验小波变换能够获得更理想的信号分解结果,包络频
谱中的故障特征频率峰值更为明显;改进方法实现了 EWT 信号分解的性能的改善,提高了滚动轴承故障诊断的可靠性。 |
| 英文摘要: |
| A modified fast empirical wavelet transform (MFEWT) based on compromise threshold function was proposed in order to solve
the problem of improper segmentation caused by soft threshold function in fast empirical wavelet transform (FEWT). For this method,
the trend spectrum is firstly calculated by Fourier transform and inverse Fourier transform and the result of calculation is optimized by
wavelet denoising with compromise threshold function. Then, filter bands are built with optimized trend spectrum and the reconstruction
of EWT empirical modes are made according to filter bands. With the fusion of kurtosis and Pearson correlation coefficient, characteristic
components are selected. With minimum entropy deconvolution (MED), characteristic frequency of signal reconstructed by characteristic
components can be calculated. Fault diagnosis of rolling bearing is finished with the comparison between characteristic frequency in
experiment and theory at last. Results of experiment demonstrated that MFEWT performed better than FEWT in signal decomposition.
For MFEWT, peaks of characteristic frequency in envelope spectra are clearer. The MFEWT improves the performance of signal
decomposition of EWT and the reliability of rolling bearing fault diagnosis. |
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