国外文献 > 1.轴承 > 5. 监测与诊断 > 状态监测
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| 标 题： |
A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing
改进的希尔伯特-黄变换与小波变换的比较研究：应用于滚动轴承故障诊断 |
| 作 者： |
Z. R. Peng
Peter W. Tse
F. L. Chu |
| 关键词： |
Wavelet transform [小波变换]
Hilbert-Huang transform [ 希尔伯特-黄变换]
Fault diagnosis [ 故障诊断]
Vibration signal [ 振动信号] |
| 出版年： |
2005 |
| 期刊名： |
Mechanical Systems and Signal Processing
机械系统与信号处理 |
| 年卷期： |
2005, vol.19, no.5 |
| 摘 要： |
For rolling bearing fault detection, it is expected that a desired time-frequency analysis method should have good computation efficiency, and have good resolution in both time domain and frequency domain. As the best available time-frequency method so far, the wavelet transform still cannot fulfill the rolling bearing fault detection task very well since it has some inevitable deficiencies. The recent popular time-frequency analysis method, Hilbert-Huang transform (HHT), has good computation efficiency and does not involve the concept of the frequency resolution and the time resolution. So the HHT seems to have potential to become a perfect tool for rolling bearing fault detection. However, in practical applications, the HHT also suffers from some unsolved deficiencies. To ameliorate these deficiencies, by seeking help from the wavelet packet transform (WPT) and a simple but effective method for intrinsic mode function (IMF) selection, an improved HHT is put forward in this studying. Several numerical study cases will be used to validate the capabilities of the improved HHT. Finally, the improved HHT's performance in rolling bearing fault detection is compared with that of the wavelet based scalogram through experimental case studies. The comparison results have shown that (1) the improved HHT has better resolution both in time domain and in frequency domain than the scalogram; (2) the improved HHT has better computing efficiency than scalogram; (3) the HHT spectrum also has one unresolved and maybe inevitable deficiency - ripple phenomenon in its estimated frequency, which would mislead our analysis.
在滚动轴承故障检测中，理想的时频分析方法应具备良好的计算效率，并在时域和频域均具有优良的分辨率。目前可用的最佳时频方法——小波变换（Wavelet Transform）仍存在一些不可避免的缺陷，难以很好地完成滚动轴承故障检测任务。近年来广受关注的时频分析方法希尔伯特-黄变换（Hilbert-Huang Transform, HHT）具有良好的计算效率，且无需引入频率分辨率和时间分辨率的概念，因此HHT似乎有望成为滚动轴承故障检测的理想工具。然而，在实际应用中，HHT也存在一些尚未解决的问题。为改善这些不足，本文提出一种改进的HHT方法，通过借鉴小波包变换（Wavelet Packet Transform, WPT）以及一种简单而有效的本征模函数（IMF）选择方法。将采用多个数值试验案例验证改进HHT方法的有效性。最后，通过实验案例研究对改进型HHT在滚动轴承故障检测中的性能与小波基小波图进行了比较。比较结果表明：（1）改进型HHT在时域和频域方面均优于小波图； (2)) 改进后的HHT比小波图具有更好的计算效率； (3) HHT频谱在估计频率上还存在一个无法解析且可能不可避免的缺陷——即纹波现象，这会误导我们的分析。 |
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