Enhanced Detection of Bearing Faults Based on Adaptive Multi-scale Self-complementary Top-Hat Transformation

Abstract

Aiming at the difficulty of extracting impulsive fault feature of rolling element bearings in practical engineering,a novel method named adaptive multi-scale self-complementary Top-Hat(AMSTP) transformation is proposed to enhance detection of bearing faults. It can enhance the impulsiveness of the bearing fault vibration signal and depress strong background noise,and constructing multi-scale is better to depress noise and retain detail of signal. The most optimal structure element(SE) scale is selected by using a novel method of feature amplitude energy radio(FAER),and it is applied in detecting fault feature of impulsive signal successfully. The performance of the proposed method is validated by both simulated signal and vibration signals of defective rolling element bearings with ball and inner faults. In addition the method could achieve better effect on feature extraction and have more operation efficiency than open-closing and close-opening combined morphological method based on signal noise ratio(SNR) criterion.