Fault feature extraction for rolling element bearings based on multi-scale morphological filter and frequency-weighted energy operator

In order to extract impulse components from bearing vibration signals with strong background noise, a fault feature extraction method based on multi-scale average combination difference morphological filter and Frequency-Weighted Energy Operator is proposed in this paper. The average combination difference morphological filter (ACDIF) is used to enhance the positive and negative impulse components in the signal. The double-dot structure element (SE) is used instead of zero amplitude flat SE to improve the effectiveness of fault feature extraction. The weight coefficients of the filtered results at different scales in multi-scale ACDIF are adaptively determined by an optimization algorithm called hybrid particle swarm optimizer with sine cosine acceleration coefficients (H-PSO-SCAC). At last, as the Frequency-Weighted Energy Operator (FWEO) outperforms the enveloping method in detecting impulse components of signals, the filtered signal is processed by FWEO to extract the fault features of bearings. Results on simulation and experimental bearing vibration signals show that the proposed method can effectively suppress noise and extract the fault features from bearing vibration signals.