Application of Optimal Morlet Wavelet Filter for Bearing Fault Diagnosis

When localized fault occurs in a bearing, the periodic impulsive feature of the vibration signal appears in time domain and the corresponding Bearing Characteristic Frequencies (BCFs) emerge in frequency domain. The common technique of Fast Fourier Transforms (FFT) and Envelope Detection (ED) are always used to identify faults occurring at the BCFs. In the early stage of bearing failures, the BCFs contain very little energy and are often overwhelmed by noise and higher-level macro-structural vibrations. In order to extract the weak fault information submerged in strong background noise of the gearbox vibration signal, an effective signal processing method would be necessary to remove such corrupting noise and interference. Optimal Morlet Wavelet Filter and Envelope Detection (ED) are applied in this paper. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are optimized based on the maximum Kurtosis value. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an envelope enhancement is applied to the filtered signal. The proposed and the common techniques are used respectively to analyze the experimental signal with inner race fault of rolling bearings. The test stand is equipped with two dynamometers; the input dynamometer serves as internal combustion engine, the output dynamometer introduce the load on the flange of output joint shaft. The Kurtosis and pulse indicator are chosen as the evaluation of the denoising effect. The results of comparative analysis have drawn that the proposed technique is more accurate and reliable than the common technique for the fault feature extraction. Especially, it is much easier to achieve early diagnosis for bearing failure.