Abstract
The feature extraction of composite fault of gearbox in mining machinery has always been a difficulty in the field of fault diagnosis. Especially in strong background noise, the frequency of each fault feature is different, so an adaptive time-frequency analysis method is urgently needed to extract different types of faults. Considering that the signal after complementary ensemble empirical mode decomposition (CEEMD) contains a lot of pseudo components, which further leads to misdiagnosis. The article proposes a new method for actively removing noise components. Firstly, the best scale factor of multi-scale sample entropy (MSE) is determined by signals with different signal to noise ratios (SNRs); secondly, the minimum value of a large number of random noise MSE is extracted and used as the threshold of CEEMD; then, the effective Intrinsic mode functions(IMFs) component is reconstructed, and the reconstructed signal is CEEMD decomposed again; finally, after multiple iterations, the MSE values of the component signal that are less than the threshold are obtained, and the iteration is terminated. The proposed method is applied to the composite fault simulation signal and mining machinery vibration signal, and the composite fault feature is accurately extracted.
Highlights
In industrial production, the normal operation of mining machinery is the guarantee for the economic growth of enterprises
The scale factor Q of the multi-scale sample entropy will affect the MSE value, and different proposes to use MSE to optimize the decomposition of complementary ensemble empirical mode decomposition (CEEMD)
The MSE value is compared with the dynamic threshold value, and if there is an IMFs component signal greater than the threshold, it is removed, and the remaining signal is reconstructed and CEEMD decomposition is performed again
Summary
The normal operation of mining machinery is the guarantee for the economic growth of enterprises. Sci. 2020, 10, 5078 it can overcome the endpoint effects of EMD and weaken the mode aliasing phenomenon, so it has been applied in industrial production by a large number of scholars It needs to set the times of additions and the amplitude of white noise; so far, no formula can adaptively determine the white noise amplitude value. Variable mode decomposition (VMD) [19,20,21] has been applied to fault diagnosis It can decompose the original signal into several different time scale functions from low frequency to high frequency. The feasibility of the method is verified by comparison with the traditional CEEMD
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