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【正文】 bearing fault diagnosis and rotor fault diagnosis. EMD deposes the plicated signal into a set of plete and almost orthogonal ponents named intrinsic mode function (IMF). However, it still has some shortings when it es to calculating instantaneous frequency or in some cases it may reveal plausible characteristics due to the mode mixing, and this shorting makes it untenable in effective application in transient detection and analysis. ICA is known as a powerful tool for blind source separation, which has also been introduced and applied to vibration analysis. ICA can be seen as an extension to principal ponent analysis (PCA) which aims at recovering the source signals from the set observed instantaneous linear mixtures without any a priori knowledge of the mixing system. Some researches applied it to extract the feature of vibration signals and detect transients. Though ICA is effective in blind separation of simulation signals, however, because of the multiple sources, intricate and varying transfer path in mechanical system and noise pollution, ICA is still in its infancy for effective application in mechanical fault diagnosis. TFR is the most frequently used method, through which the transient feature can be represented in the twodimensional time–frequency plane. For example, the Wigner–Ville distribution (WVD) and improved Wigner–Ville distribution have been utilized to depose vibration signals for fault diagnosis. It is no doubt that WVD has good concentration in the time–frequency plane. However, these methods are bilinear in nature, and there exist cross items in the deposition 3 results that can interfere in the feature interpretation. Even though some improved methods have been proposed, such as Choi–Willams distribution, coneshaped distribution and so on, without exception, however, elimination of one shorting will always lead to the loss of other merits. For example, the reduction of interference terms will bring the loss of time–frequency concentration. The wavelet transform, which is actually a kind of time–frequency analysis method, provides the signal information in the time and the frequency domains simultaneously through a series of convolution operations between the signal being analyzed and the base wavelet under different scaling parameters. The application of the wavelet transf
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