Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission

Gao, Zheyu; Lin, Jing; Wang, Xiufeng; Xu, Xiaoqiang

doi:10.3390/ma10060571

Cited by 27 publications

(16 citation statements)

References 19 publications

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“…The parameter identification of mechanical systems [ 136 , 137 ] and isolation of deferent wave packages in ultrasonic non-destructive testing [ 67 ] belong to this category. For the latter, highlighting specific characteristics of valuable components is the filter target, and the loss of the valuable component and the residual noise (invaluable components can be taken as noise in this paper) are tolerable, for example in fault diagnosis of rolling bearings [ 138 , 139 , 140 ]. The algorithms mentioned above can be taken as different filters, and have their respective applicable scopes, inapplicable scopes and further research issues, as summarized in Table 3 and Table 4 .…”

Section: Summary and Prospectsmentioning

confidence: 99%

A Comparative Study of Four Kinds of Adaptive Decomposition Algorithms and Their Applications

Liu

Luo

Huang

et al. 2018

Sensors

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The adaptive decomposition algorithm is a powerful tool for signal analysis, because it can decompose signals into several narrow-band components, which is advantageous to quantitatively evaluate signal characteristics. In this paper, we present a comparative study of four kinds of adaptive decomposition algorithms, including some algorithms deriving from empirical mode decomposition (EMD), empirical wavelet transform (EWT), variational mode decomposition (VMD) and Vold–Kalman filter order tracking (VKF_OT). Their principles, advantages and disadvantages, and improvements and applications to signal analyses in dynamic analysis of mechanical system and machinery fault diagnosis are showed. Examples are provided to illustrate important influence performance factors and improvements of these algorithms. Finally, we summarize applicable scopes, inapplicable scopes and some further works of these methods in respect of precise filters and rough filters. It is hoped that the paper can provide a valuable reference for application and improvement of these methods in signal processing.

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Section: Summary and Prospectsmentioning

confidence: 99%

A Comparative Study of Four Kinds of Adaptive Decomposition Algorithms and Their Applications

Liu

Luo

Huang

et al. 2018

Sensors

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“…By developing a feasible and efficient spectrum segmentation method, Yang et al [37] also proposed an enhanced EWT and validated the effectiveness of this method by using the bearing and gearbox signals. In 2016, Lin et al [38] utilized EWT to decompose the acoustic emission (AE) signals to extract the bearing fault feature by calculating the correlated kurtosis (CK) of the mono-components. However, a suitable time interval in the calculation of CK is difficult to pre-define and the performance of the proposed method is not good enough for engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction and Enhancement of Rolling Element Bearings Based on Maximum Correlated Kurtosis Deconvolution and Improved Empirical Wavelet Transform

Ming

Zhang

et al. 2019

Applied Sciences

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In order to extract and enhance the weak fault feature of rolling element bearings in strong noise conditions, the Empirical Wavelet Transform (EWT) is improved and a novel fault feature extraction and enhancement method is proposed by combining the Maximum Correlated Kurtosis Deconvolution (MCKD) and improved EWT method. At first, the MCKD method is conducted to de-noise the signal by eliminating the non-impact components. Then, the Fourier spectrum is segmented by local maxima or minima in the envelope of the amplitude spectrum with a pre-set threshold based on the noise level. By building up the wavelet filter banks based on the spectrum segmentation result, the signal is adaptively decomposed into several sub-signals. Finally, by choosing the most meaningful sub-signal with the maximum kurtosis, the fault feature can be extracted in the squared envelope spectrum and teager energy operator spectrum of the chosen component. Both simulations and experiments are performed to validate the effectiveness of the proposed method. It is shown that the spectrum segmentation result of improved EWT is more reasonable than the traditional EWT in strong noise conditions. Furthermore, compared with commonly used methods, such as the Fast Kurtogram (FK) and the Optimal Wavelet Packet Transform (OWPT) method, the proposed method is more effective in the fault feature extraction and enhancement of rolling element bearings.

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“…Wavelet transform, derived from the classical short time Fourier transform, enables the separation of different modes into a number of localized time-frequency subspaces. Currently there are many variations of wavelet transform which have been successfully used to conduct condition monitoring [11,12]. Hemmati extracted bearing characteristic frequencies from the raw acoustic emission signals masked by background noise using wavelet packet transform [13].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Fault Feature Extraction from Displacement Signals by Suppressing Severe End Distortions via Sinusoidal Wave Reduction

Chen

Lan

et al. 2019

Energies

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Displacement signals, acquired by eddy current sensors, are extensively used in condition monitoring and health prognosis of electromechanical equipment. Owing to its sensitivity to low frequency components, the displacement signal often contains sinusoidal waves of high amplitudes. If the digitization of the sinusoidal wave does not satisfy the condition of full period sampling, an effect of severe end distortion (SED), in the form of impulsive features, is likely to occur because of boundary extensions in discrete wavelet decompositions. The SED effect will complicate the extraction of weak fault features if it is left untreated. In this paper, we investigate the mechanism of the SED effect using theories based on Fourier analysis and wavelet analysis. To enhance feature extraction performance from displacement signals in the presence of strong sinusoidal waves, a novel method, based on the Fourier basis and a compound wavelet dictionary, is proposed. In the procedure, ratio-based spectrum correction methods, using the rectangle window as well as the Hanning window, are employed to obtain an optimized reduction of strong sinusoidal waves. The residual signal is further decomposed by the compound wavelet dictionary which consists of dyadic wavelet packets and implicit wavelet packets. It was verified through numerical simulations that the reconstructed signal in each wavelet subspace can avoid severe end distortions. The proposed method was applied to case studies of an experimental test with rub impact fault and an engineering test with blade crack fault. The analysis results demonstrate the proposed method can effectively suppress the SED effect in displacement signal analysis, and therefore enhance the performance of wavelet analysis in extracting weak fault features.

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Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission

Cited by 27 publications

References 19 publications

A Comparative Study of Four Kinds of Adaptive Decomposition Algorithms and Their Applications

A Comparative Study of Four Kinds of Adaptive Decomposition Algorithms and Their Applications

Fault Feature Extraction and Enhancement of Rolling Element Bearings Based on Maximum Correlated Kurtosis Deconvolution and Improved Empirical Wavelet Transform

Enhancement of Fault Feature Extraction from Displacement Signals by Suppressing Severe End Distortions via Sinusoidal Wave Reduction

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