Compound fault identification of rolling element bearing based on adaptive resonant frequency band extraction

Abstract

The high frequency resonance (HFR) technique is regarded as a powerful tool for fault diagnosis of rolling element bearings. Different from the usage of the HFR in single fault, the determination of multiple resonant frequency bands under the compound faults and extraneous random impulses is still a challenging task. This paper develops a novel compound fault identification method based on adaptive resonant frequency band extraction. The improved redundant second generation wavelet packet transform is first presented to decompose vibration signal into various narrow bands for providing a fine separation of fault signatures. Then the squared envelope spectrum sparsity criteria is designed to quantify fault characteristics buried in narrow frequency bands. Consequently, the squared envelope spectrum sparsogram is constructed to highlight optimal resonant bands, and the compound faults can be well detected by band-pass filtering and envelope analysis. The numerical and experimental results confirm effectiveness and superiority of the proposed method, which is more sensitive to fault-related impulses and robust to extraneous interferences.