Teager energy spectral kurtosis of wavelet packet transform and its application in locating the sound source of fault bearing of belt conveyor

Kurtogram is widely used in fault diagnosis because it can adaptively identify the resonance frequency band of the fault. However, kurtosis defined in time domain has poor robustness and is very sensitive to noise and non-periodic transient impulse caused by accidental vibration, thus these components easily lead to disturbance in kurtogram algorithm. In this study, a frequency domain statistical index called teager energy spectral kurtosis (TESK) was proposed. This index combines the enhancement of teager energy operator to impulse characteristics and the sensitivity of frequency domain index to periodic characteristics, thus it can easily capture the resonance frequency band of fault characteristics in sound signals of bearing well. Through simulated and experimental analysis, TESK can well represent the sub-band wavelet packet transform, so as to extract the sub-band with relatively concentrated fault information. Further, it is combined with generalized cross-correlation post-processing, and finally the position of fault sound source can be located.

Automated summary

In this study, a frequency domain statistical index called Teager energy spectral kurtosis (TESK) was proposed to easily capture the resonance frequency band of fault characteristics in sound signals of bearing well and the position of fault sound source can be located through simulated and experimental analysis, further combining with generalized cross-correlation post-processing.