Abstract
Aiming at the problem of feature extraction of non-stationary, non-linear and weak fault signals, a new feature extraction method based on empirical wavelet transform (EWT) with scale space threshold (STEWT) and improved maximum correlation kurtosis deconvolution (MCKD) with power spectral entropy and grid search (PGMCKD), namely STEWT-PGMCKD is proposed for rolling bearing faults in this paper. In the proposed STEWT-PGMCKD method, the scale space threshold method is designed to solve the problems of falling into local extremum and mode over decomposition caused by the local-max-min band decomposition method of EWT, which is used to decompose the frequency band of signal, and the correlation analysis is carried out between the decomposed modal components and the original signal to retain the modal components with high correlation. Then an adaptive MCKD based on power spectral entropy is proposed to solve the problem that the signal processing effect of MCKD is affected by filter size L and deconvolution period T. Nextly, the parameters of the MCKD are optimized by grid search method. Finally, the power spectrum analysis of the enhanced signal is carried out to realize the feature extraction and fault diagnosis. The experiment results show that the proposed STEWT-PGMCKD method can effectively extract the weak fault information and accurately realize the fault diagnosis for rolling bearings.
Highlights
As the core component of typical rotating machinery, rolling bearing plays an important role in the effective operation of the whole mechanical system, and becomes one of the components with frequent faults
A NOVEL FAULT DIAGNOSIS METHOD In order to solve the problem that the fault signal of rolling bearings is weak and it is difficult to extract fault feature under strong noise and complex transmission path, a new feature extraction method based on empirical wavelet transform (EWT) with scale space threshold and maximum correlation kurtosis deconvolution (MCKD) with power spectral entropy and grid search is proposed to realize the fault diagnosis of rolling bearings in this paper
Aiming at the problem of feature extraction of rolling bearing fault signal, a feature extraction method based on EWT with scale space threshold and improved MCKD with power spectral entropy and grid search is proposed to realize the fault diagnosis of rolling bearings in this paper
Summary
As the core component of typical rotating machinery, rolling bearing plays an important role in the effective operation of the whole mechanical system, and becomes one of the components with frequent faults. Shen et al [47] proposed a novel signal processing method based on the particle swarm optimization, maximum correlated kurtosis deconvolution, variational, mode decomposition and fast spectral kurtosis (PSO-MCKD-VMD-FSK) to extract fault characteristics for the signal-to-noise ratio and uneven energy distribution problems. Aiming at the deficiency of local local-max-min method of EWT for interval segmentation, the EWT with scale space threshold is deeply studied, and the an adaptive MCKD based on power spectral entropy is proposed to solve the problem that the signal processing effect of MCKD is affected by filter size L and deconvolution period T. EMPIRICAL WAVELET TRANSFORM WITH SCALE SPACE THRESHOLD EWT is a new signal processing method proposed by Gilles, which extracts different AM-FM components of signal by adaptively selecting a set of wavelet filter banks according to the Fourier spectrum characteristics of signal. That we can use fuzzy C-mean method to divide the set {Li}i=[1,N0] clusters into two clusters (meaningful and non-meaningful minima)
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