Abstract
Mechanical signals are not only disturbed by Gaussian noise, but also by non-Gaussian noise. These Gaussian noise and non-Gaussian noise have gravely impeded detecting of rolling bearing defects using traditional methods. In this context, the paper develops a novel detection method for rolling bearing, which combines bispectrum analysis with an improved ensemble empirical mode decomposition (EEMD). To effectively eliminate Gaussian noise in the signal, bispectrum analysis is adopted. In order to effectively reduce non-Gaussian noise, a cloud model-improved EEMD is proposed, where the cloud model is introduced to restrain the mode mixing phenomenon. Then a rolling bearing defect detection plan based on the proposed method is put forward. From theoretical analysis and experimental verification, it is demonstrated that the proposed method has superior performance in reducing multiple background noise. Furthermore, compared with other three methods, the results show that the proposed method can detect the defect of rolling bearings more effectively.
Highlights
As a widely used and important part of the rotating machine, rolling bearings often lead to serious consequences of mechanical equipment after their failure
Huang et al [7] use the performance of the conventional bispectrum (CB) method and its new variant, the modulation signal bispectrum (MSB) method to analyze faults from different rotors, and prove that MSB performs significantly better than the CB method
Jiang et al.: Novel Rolling Bearing Defect Detection Method of complex noise, especially containing Gaussian noise, the non-Gaussian noise will inevitably exist in the measured vibration signals, which often inhibit the extraction of true signal signatures for diagnosis
Summary
As a widely used and important part of the rotating machine, rolling bearings often lead to serious consequences of mechanical equipment after their failure. Jiang et al.: Novel Rolling Bearing Defect Detection Method of complex noise, especially containing Gaussian noise, the non-Gaussian noise will inevitably exist in the measured vibration signals, which often inhibit the extraction of true signal signatures for diagnosis To improve these methods, Li et al [3] propose a feature extraction method based on wavelet transform and bispectrum analysis which can extract the bearing fault features effectively but cannot detect the multi-frequency component of the fault. The signal is decomposed by EEMD into several IMFs with different frequencies from high to low, and the cloud model method is used to remove the false IMFs. the bispectrum analysis of the true IMFs is carried out to extract the fault feature information of the fault rolling bearing.
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