A three-stage method for efficiently extracting the higher-order harmonics of bearing fault characteristic frequencies

Weak bearing fault feature extraction (FFE) research has previously focused on bearing fault signal of transient impulse extraction whereas higher harmonic feature extraction of fault feature frequency aspects are relatively less. While traditional envelope demodulation method has certain limitations to capture the rolling bearing fault characteristic frequency of higher harmonic. To this end, combining adaptive chirp mode decomposition (ACMD), improved maximum correlation kurtosis deconvolution (IMCKD), and 1.5-dimensional Teager energy cyclo-stationary spectrum (1.5-DTECS), in the current work we propose a three-stage defect detection system. The performance of the proposed method is evaluated by analyzing the three-stage FFE (STFFE) of multiple kinds of rolling bearing fault data. The findings reveal that the three-stage fault detection approach successfully suppresses noise, highlights the fault impact, and extracts the higher order harmonics of the bearing defect characteristic frequency more effectively. The research contributes to the field of bearing fault high harmonic component extraction and provides guidance on techniques related to the extraction of bearing impulse characteristic.

Automated summary

This study proposes a three-stage defect detection system combining adaptive chirp mode decomposition, improved maximum correlation kurtosis deconvolution, and 1.5-dimensional Teager energy cyclo-stationary spectrum. The system successfully extracts higher order harmonics of bearing defect characteristic frequency, suppresses noise, and highlights fault impact.