Rolling Bearing Fault Feature Extraction Method Using Adaptive Maximum Cyclostationarity Blind Deconvolution

Maximum cyclostationarity blind deconvolution (CYCBD) is an effective method for extracting bearing weak fault impulse. However, the CYCBD-based bearing fault diagnosis method has the following problems: setting the cyclic frequency and filter length requires artificial experience guidance and improper settings lead to incorrect diagnostic results. To this end, a novel method of parameter adaptive maximum cyclostationarity blind deconvolution (ACYCBD) is proposed for bearing fault diagnosis. This method analyzes the cyclostationarity of the signal by the fast spectral correlation (Fast-SC) algorithm and obtains the enhanced envelope spectrum (EES) of the signal. The cyclic frequency is accurately estimated using envelope harmonic product spectrum (EHPS) based on the harmonic-related spectral structure (HRSS) in EES. Finally, the filter length is determined by the envelope entropy efficiency assessment (EEEA) index. The proposed ACYCBD-based bearing fault diagnosis method is validated by simulated signal and experimental data to effectively extract weak fault impulses from bearing vibration observation signal without prior knowledge.