Study On Faults Diagnosis Of Rolling Element Bearing Based On Wavelets Analysis

Faults Diagnosis of rolling element bearing plays an important role in Machinery Fault Diagnostics. Wavelet analysis as a new signal processing method has been developed about twenty years and has been verified successfully in nonstationary signal processing. Most vibration signals from faults in rolling element bearing belong to nonstationary signals. This thesis integrates these two parts together, in which wavelet analysis is used in faults diagnosis in rolling element bearing successfully and a new approach based on continuous wavelet transform is presented. At the same time, the validity of the proposed method is confirmed by verification test.Many signals from faults in rolling element bearing are analyzed by wavelet transform in the paper. The proposed approach can be employed to obtain the fault-character frequency of rolling element bearings by Fourier transform which decomposes the coefficients from continuous wavelet transform on a certain scale. The selection of the scale used in the method is based on the scale-power spectrogram of continuous wavelet transform. It can be used to pick up the characteristic frequency of faults in rolling element bearing accurately under a strong noise background.Effects of different wavelet on analyzing the character frequency of faults in rolling element bearing are compared in the research at the same time. The results from experiments verified that Morlet wavelet and Meyer wavelet have much better effect on exposing the character frequency of faults in rolling element bearing than other wavelets. On the other hand, the experiments have shown that some quality of Discrete Meyer wavelet is better than Morlet wavelet for the fault diagnosis of rolling element bearing.