A novel spectral coherence-based envelope spectrum for railway axle-box bearing damage identification

Damage identification of axle-box bearings is essential to ensure the safe operation of railway trains. The envelope spectrums generated by spectral coherence are effective bearing damage identification tools, but the traditional spectral coherence-based envelope spectrums cannot effectively reveal the bearing damage features under strong interference noise or cannot fully extract the damage information distributed in multiple spectral frequency bands. To solve these problems, a weighted combined envelope spectrum (WCES) based on spectral coherence is proposed as an enhanced bearing damage detector in this paper. First, the frequency domain signal-to-noise ratio (FDSNR) is devised to measure the damage information in each spectral frequency component of spectral coherence. Then, an information threshold is introduced into the estimated FDSNR to construct a weighting function to enhance the informative spectral frequency components and eliminate the interference components. Eventually, the spectral coherence normalized by the weighting function is integrated to generate WCES for bearing damage identification. The simulation and experimental results indicate that the proposed method can effectively excavate the fault-related information and detect railway axle-box bearing damages, and the comparisons with the state-of-the-art methods demonstrate the superiority of the proposed method.

Automated summary

This paper proposes a weighted combined envelope spectrum (WCES) based on spectral coherence for bearing damage detection. By introducing frequency domain signal-to-noise ratio and a weighting function, this method can effectively reveal and extract bearing damage information.