Study Of Improved EMD And Its Application In Gear Fault Diagnosis

With the rapid development of signal processing technology, the time-frequency analysis method has already become one of the most important and effective ways in processing non-stationary and nonlinear signals, which is a major breakthrough in signal processing methods because more ample characteristics of signals are studied from the view of the time domain and frequency domain synchronously. Especially, Empirical Mode Decomposition (EMD for short) theory, first proposed by the Chinese-American N.E.Huang in 1998, has aroused more and more attention in recent years as a significant time-frequency method of signal processing.In this paper, several time-frequency methods of signal processing are briefly introduced, including their advantages and disadvantages. At the same time, EMD, together with the reason for its defects in endpoint effect which exist in its decomposition iterative algorithm is discussed in detail. And then, a new method is put forward to restrain the serious endpoint effect of EMD in dealing with nonlinear and non-stationary signals by using the GM (1,1) model of Grey to extend some endpoints at both sides of original data sequence, and the data sequence extended, which reflect inner information and development trend of the original data sequence on the whole, could ensure that the upper and lower envelope obtained from three cubic interpretation are out of generating endpoint effect in the process of sifting. In this way, the endpoint effect does not destroy the internal information of original data sequence. Thus, this method greatly restrains the end effect of EMD and makes the Intrinsic Mode Function (IMF for short) obtained from the original data sequence reliable and effective. Furthermore, gear is one of the most important machine transmission parts, so the method of gear fault diagnosis plays a vital role in promoting modern industrial development. The gear faults such as teeth broken,n teeth wear and so on, will result in gear mesh impact vibration containing much periodicity fault impact components. And the improved EMD is used to find gear fault character from gear impact vibration signal. It is a reliable approach to predict and diagnose early gear fault.Experiment is the basic way to obtain data and verify theory. The data in this paper are completely obtained from gear physical simulation experiment in which the gear vibration signals in normal and fault condition are respectively collected. Then the experimental data are dealt with depending on the software MATLAB.Based on the above experimental data, the same gear vibration signal is decomposed into different IMFs by using improved EMD whose endpoint effect is restrained based on GM (1,1) and unimproved EMD whose endpoint effect is not, and the results show that the former is more effective than the latter. Equally, the gear fault vibration signals and the normal gear vibration signals are also divided into several IMFs by using improved EMD and unimproved EMD respectively. And then the IMFs obtained from fault and normal gear vibration signals are respectively transformed into Hilbert-Huang spectrum and Hilbert marginal spectrum. At last, the result of comparing Hilbert-Huang spectrum and Hilbert marginal spectrum obtained from gear fault vibration signals with those from normal gear vibration signals remarkably reveals the existing of gear fault character and also proves the GM (1,1) model can effectively restrain the endpoint effect of EMD.