Abstract
When the vibration signals of the rolling bearings contain strong interference noise, the spectrum division of the vibration signals is seriously disturbed by the noise. The traditional empirical wavelet transform (EWT) decomposes signals into a large number of components, and it is difficult to select suitable components that contain fault information. In order to address the problems above, in this paper, we proposed the improved empirical wavelet transform (IEWT) method. The simulation experiment proved that IEWT can solve the problem of a large number of EWT components and separate the impact component effectively which contains bearing fault information from noise. The IEWT method is combined with the support vector machine (SVM) to diagnosis the fault of the rolling bearings. The permutation entropy (PE) is used to construct feature vectors for its strong induction ability of dynamic changes of nonstationary and nonlinear signals. The crucial parameter penalty factor C and kernel parameter g of SVM are optimized by quantum genetic algorithm (QGA). Compared with traditional EWT and variational mode decomposition (VMD) methods, the effectiveness and advantages of this method are demonstrated in this study. The classification prediction ability of SVM is also better than that of K-nearest neighbor (KNN) and extreme learning machine (ELM).
Highlights
When the vibration signals of the rolling bearings contain strong interference noise, the spectrum division of the vibration signals is seriously disturbed by the noise. e traditional empirical wavelet transform (EWT) decomposes signals into a large number of components, and it is difficult to select suitable components that contain fault information
In order to address the problems above, in this paper, we proposed the improved empirical wavelet transform (IEWT) method. e simulation experiment proved that IEWT can solve the problem of a large number of EWT components and separate the impact component effectively which contains bearing fault information from noise. e IEWT method is combined with the support vector machine (SVM) to diagnosis the fault of the rolling bearings. e permutation entropy (PE) is used to construct feature vectors for its strong induction ability of dynamic changes of nonstationary and nonlinear signals. e crucial parameter penalty factor C and kernel parameter g of SVM are optimized by quantum genetic algorithm (QGA)
EWT has a great advantage in signal decomposition without mode-mixing and running fast
Summary
According to the wavelet theory, the empirical mode function is obtained from the following equation: x0(t) Wεf(0, t) ∗ φ1(t),. Permutation entropy is a measure of the complexity of time series, which has a good ability to analyze the complexity and sense the small changes of nonstationary and nonlinear signals and good resistance of noise. It shows great advantages in representing the fault state of vibration signals of rotating machinery [21, 22]. In [25], the authors indicate that when m 5∼7, the best effect can be obtained by using the permutation entropy to characterize the dynamic change of time series. In [25], the authors indicate that when m 5∼7, the best effect can be obtained by using the permutation entropy to characterize the dynamic change of time series. rough experimental verification, when m 5, it has higher operational efficiency, so m 5 is taken in this paper
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