Rolling element bearings fault diagnosis based on physical model identification

In order to solve the problem of small-samples and incipient fault prognosis, a novel identification approach based on physical model is presented for automatic diagnosis of defective rolling element bearings. The major advantage of this method is that its training can be performed using simulation data. Prediction of the vibration response due to defect requires an accurate model. Multi-body dynamics of rolling element bearing are developed according to the vibration transmission path combining with dynamics contact mechanism of interface. For the purpose of extracting the feature of weak impact component, a new detecting method based on Blind deconvolution and Kurtosis-Laplace wavelet is proposed. The simulation and the detection of engineering faint impact signal results demonstrate that this method is highly effective in noise reduction and fault feature extraction. Then, through translating the inverse problem into geometric distance matching, the defects can be predicted. Finally, experimental data is used to verify the feasibility and reliability of current method.