A Novel Rolling Bearing Defect Detection Method Based on Bispectrum Analysis and Cloud Model-Improved EEMD
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TLDR
A novel detection method for rolling bearing is developed, which combines bispectrum analysis with an improved ensemble empirical mode decomposition (EEMD) to effectively eliminate Gaussian noise in the signal.Abstract:
Mechanical signals are not only disturbed by Gaussian noise, but also by non-Gaussian noise. These Gaussian noise and non-Gaussian noise have gravely impeded detecting of rolling bearing defects using traditional methods. In this context, the paper develops a novel detection method for rolling bearing, which combines bispectrum analysis with an improved ensemble empirical mode decomposition (EEMD). To effectively eliminate Gaussian noise in the signal, bispectrum analysis is adopted. In order to effectively reduce non-Gaussian noise, a cloud model-improved EEMD is proposed, where the cloud model is introduced to restrain the mode mixing phenomenon. Then a rolling bearing defect detection plan based on the proposed method is put forward. From theoretical analysis and experimental verification, it is demonstrated that the proposed method has superior performance in reducing multiple background noise. Furthermore, compared with other three methods, the results show that the proposed method can detect the defect of rolling bearings more effectively.read more
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References
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Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Journal ArticleDOI
Ensemble empirical mode decomposition: a noise-assisted data analysis method
Zhaohua Wu,Norden E. Huang +1 more
TL;DR: The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF.
Journal ArticleDOI
Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery
TL;DR: Results fully demonstrate that the stacked SAE-based diagnosis method can extract more discriminative high-level features and has a better performance in rotating machinery fault diagnosis compared with the traditional machine learning methods with shallow architectures.
Journal ArticleDOI
Performance enhancement of ensemble empirical mode decomposition
TL;DR: In this article, a modified ensemble empirical mode decomposition (MEEMD) method is proposed to reduce the computational cost of the original EEMD method as well as improving its performance.
Journal ArticleDOI
Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment
TL;DR: In this paper, a modified bispectrum based on the amplitude modulation feature of the current signal is adopted to combine both lower sidebands and higher sidebands simultaneously and hence characterise the current signals more accurately.