A narrowband envelope spectra fusion method for fault diagnosis of rolling element bearings

Narrowband amplitude demodulation is an effective tool for extracting characteristic features in the fault diagnosis of rolling element bearings. The quality of demodulation largely depends on the frequency band selected for the demodulation. Numerous criteria have been constructed to determine the optimal frequency band. However, independent frequency interferences and in-band noises in narrowband signals can greatly affect the values of the criteria, which may lead to an inaccurate result in locating the optimal band, in demodulating fault features, and finally in the fault detection. Inspired by the nonlocal means (NL-means) denoising method that has been widely used in image processing, this paper proposes a narrowband envelope spectra fusion (NESF) method to enhance fault features and suppress in-band noises before criterion calculation. The method suppresses in-band noises by averaging envelope spectra at neighboring narrow bands. Meanwhile, some minor improvements are made to the conventional narrowband envelope spectrum calculation method to enhance the similarity of the narrowband envelope spectra containing fault features, and finally optimize the fusion process. Then, sparsity values of these denoised envelope spectra, which can lower the impact of independent frequency interferences, are utilized to determine the optimal band and select the optimal envelope spectrum. Frequency signatures of the extracted envelope spectrum can be utilized to indicate the status and fault types of rolling element bearings. A simulated bearing fault signal and three real bearing fault signals are used to validate the effectiveness of the proposed method through comparison studies with protrugram and sparsogram. The results show that the proposed method can effectively extract fault characteristics, even in a harsh environment.