CFFsgram: A candidate fault frequencies-based optimal demodulation band selection method for axle-box bearing fault diagnosis

How demodulate the vibration signal is an essential strategy for revealing the weak fault symptomatic of axle-box bearings. This paper proposed a candidate fault frequencies (CFFs)-based method, abbreviated as CFFsgram, for the optimal demodulation frequency band (DFB) identification of the axle-box bearing. The 1/3-binary tree filter bank constructed by empirical wavelet transform is adopted to divide the vibration signal into different narrowband with the same length. The local features of the squared envelope spectra of the narrowband signals are fully mined to identify the CFFs-a collection of frequencies most likely to be associated with bearing fault. An indicator calculated on the SESs of the narrowband signals is designed to guide the selection of the DFB. The superiority of the CFFsgram in resisting strong noise and random impulses is verified and confirmed by using four different challenging experimental datasets.

Automated summary

This paper proposes a method called CFFsgram based on candidate fault frequencies (CFFs) for the optimal demodulation frequency band (DFB) identification of axle-box bearings. The vibration signal is divided into different narrowbands using a 1/3-binary tree filter bank constructed by empirical wavelet transform. The local features of the squared envelope spectra of the narrowband signals are used to identify the CFFs, which are frequencies most likely associated with bearing faults. An indicator calculated on the narrowband signals is designed to guide the selection of the DFB. The CFFsgram is shown to be superior in resisting strong noise and random impulses through experiments with challenging datasets.