A comparison study of improved HilbertHuang transform and wavelet transform: Application to fault diagnosis for rolling bearing
For rolling bearing fault detection, it is expected that a desired timefrequency analysis method should have good computation efficiency, and have good resolution in both time domain and frequency domain. As the best available timefrequency 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 timefrequency analysis method, HilbertHuang 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 deficiencyripple phenomenon in its estimated frequency, which would mislead our analysis.
hilberthuang transform wavelet transform fault diagnosis vibration signal