Wavelet Neural Network MATLAB Code
Gearbox transmission is one of the most popular in the machinery and equipment. At the same time, gearbox is the easy inactivation part in equipment and its abnormal is also the important factor to inducing failure of machinery. So it is very critical to diagnose gearbox faults accurately and quickly. The method we use in fault diagnose include Fourier Transform, Short Time Fourier Transform (STFT), Wigner-Vill Distribution and Wavelet Analysis usually. But these methods are base on Fourier Transform, and predominance to analyze the stable signal not the instantaneous. It can not get the signal instant frequency with temporal change.In view of the fault vibration signal non-linearity, non-stationary, the paper uses the Hilbert-Huang Transform time-frequency analysis method. The Hilbert-Huang Transform is a new method for analyzing signal, consists of two successive parts, the Empirical Mode Decomposition (EMD) and the Hilbert transform. It decomposes the complicated signal into a number of Intrinsic Mode Function (IMF), and then the Hilbert transform is performed on each IMF, and the Hilbert spectra of all IMFs are grouped to get the Hilbert spectrum of the original signal, which possesses high time-frequency resolution. So the key point of paper analyzes the basic principle, the arithmetic, and the physics significance of Hilbert-Huang Transform, shows the superiority though emulation example.Against the vibration mechanics, the typical fault feature of gearbox and the common fault diagnosis method, the Hilbert-Huang Transform is applied in elevator conveyor gearbox fault diagnosis. Firstly identify fault position of gearbox though simplify vibration measurement, and then use the Hilbert-Huang Transform to diagnosis delicately. According to analyze, compare the magnitude spectrum, the Hilbert-Huang spectrum, and the marginal spectrum, diagnosis the fault of gear abrasion and modulation effect between gears successfully.
Source: Good Mechanical Paper Studio
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