Study On The Method Of Bearing Fault Extraction Based On EMD And Wavelet Packet

First, after analyzing the characteristics and limitations of classic signal processing, the paper introduces today's research focus——Hilbert-Huang transform(HHT) and Wavelet transform(WT), which are the main method of non-stationary signals processing, and deeply studied the basic theory and algorithms.Second, this paper is focused on combining empirical mode decomposition and wavelet and point out that most of this combination is used a good reduction noise of wavelet to do pre-processing. Since the actual motor vibration signals are very complex and mix some noise, it is necessary to find a noise reduction method. However, it is difficult to get unified noise reduction model, and so it is very difficult to extract fault feature. After studying the theoreti- cal of EMD found that the decomposition process is not only self-adaptive but also adaptive filtering. Therefore, the paper combines adaptive filtering of EMD and wavelet packet decom- position(the paper is known as EMD_WP) to deal with non-stationary signal. EMD_WP is further analyzing the IMFs decomposed by wavelet packet. Simulation results show that in a case of noise, the method can still extract more accurate characteristic frequency. During the trial also demonstrates that when the noise intensity is large, with the second generation wavelet noise reduction, EMD_WP's effect will be improved.Finally, this paper with non-stationary bearing fault signals as the research object and describes the bearing defect mechanism, by using signal processing methods to extract the feature parameters. In the end, the EMD_WP applied into bearings fault feature extraction. And the result proves that it not only has the higher feasibility, but also extracts more accurate fault information.