Abstract
In order to identify the nonlinear nonstationary pitting-wear fault signal of gears in gearbox, a new method of composite fault diagnosis for gearbox is proposed, which combines empirical mode decomposition with improved variational mode decomposition. Aiming at the problem of false modes in the results of empirical mode decomposition when processing signals, the energy method is used to eliminate and update the mode components, and the correlation coefficient method is used to calculate the correlation between the updated mode components and the original signal. The components with strong correlation are selected to form the combined mode components to weaken the noise and improve the signal-to-noise ratio. Aiming at the problem that variational mode decomposition method needs to manually determine the number of mode components [Formula: see text] and the penalty factor [Formula: see text] during signal decomposition, a combination of envelope spectral entropy and waveform method is proposed to determine the optimal parameter combination. By analyzing the pitting-wear composite fault vibration signals of gears in the gearbox and the normal signals of the gearbox, the effectiveness of the proposed method is verified, and a comparative analysis with the empirical mode decomposition method is performed to highlight the superiority of the proposed method.
Highlights
Gearbox is one of the most important transmission mechanisms
Based on the above research background, this paper proposes a composite fault diagnosis method for gearbox based on empirical mode decomposition (EMD)-IVMD
On the basis of the above theory, this paper proposes a method of compound fault diagnosis of gearbox based on EMD-IVMD
Summary
Gearbox is one of the most important transmission mechanisms. The condition of the gearbox directly affects the normal operation of the mechanical equipment. Aiming at the problems of EMD, a noise-assisted analysis method—ensemble empirical mode decomposition (EEMD) is proposed In this method, the white noise amplitude is added to the original signal for continuous screening, and the average value is used as the final result, so that the signal is disturbed in the true solution neighborhood, and the adaptive separation of signals at different scales is achieved.[11] Yang et al combined multi-point optimal minimum entropy deconvolution with EEMD to separate and extract the composite fault features of gears and bearings in a gearbox.[12].
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