A new method of bearing fault diagnosis based on LMD and wavelet denoising

The impulse feature from an early diagnosis of bearing fault is often drowned by the noise background, and is usually very difficult to extract. To solve the problem, a new method was presented here, which was based on the Local Mean Decomposition (LMD) and wavelet de-noising. The LMD was used to decompose the original signal of the bearing into serval PF components which were retained using the principle of maximum kurtosis and cross-correlation coefficients to keep only the reasonable ones. Compared to the traditional PF component selection process, our new method captured more fault impulse features in the selected PF components. For these retained PF components were first de-noised by a db10 wavelet of 5 layers, and then were used to reconstruct the high frequency signal of each component layer by the method of superposition. Finally, the envelope spectrum analysis was applied to the derived the spectral kurtosis to give the result of rolling bearing fault diagnosis. A test experiment was conducted with our bearing fault simulation platform. The collected data, including signal from bearing outer ring, inner ring and ball, was analyzed using the method proposed in this paper. The result shown that the new method can effectively enhance the impulse features in the signal, also improve the fault diagnosis efficiency.