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
TLDR
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.Abstract:
A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...read more
Citations
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Journal ArticleDOI
Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings
Dejie Yu,Junsheng Cheng,Yu Yang +2 more
TL;DR: In this article, the authors proposed a method for the fault diagnosis of roller bearings based on EMD and Hilbert spectrum analysis of wavelet coefficients of high scales, which can obtain the local Hilbert marginal spectrum from which the faults in a bearing can be diagnosed and fault patterns can be identified.
Journal ArticleDOI
Machine learning methods for wind turbine condition monitoring: A review
Adrian Stetco,Fateme Dinmohammadi,Xingyu Zhao,Valentin Robu,David Flynn,Mike Barnes,John A. Keane,Goran Nenadic +7 more
TL;DR: This paper reviews the recent literature on machine learning models that have been used for condition monitoring in wind turbines and shows that most models use SCADA or simulated data, with almost two-thirds of methods using classification and the rest relying on regression.
Journal ArticleDOI
Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression
TL;DR: The experimental results show that the use of the HHT, the SVM, and the SVR is a suitable strategy to improve the detection, diagnostic, and prognostic of bearing degradation.
Journal ArticleDOI
A roller bearing fault diagnosis method based on EMD energy entropy and ANN
Yang Yu,YuDejie,Cheng Junsheng +2 more
TL;DR: The analysis results from roller bearing signals with inner-race and out-race faults show that the diagnosis approach based on neural network by using EMD to extract the energy of different frequency bands as features can identify roller bearing fault patterns accurately and effectively and is superior to that based on wavelet packet decomposition and reconstruction.
Journal ArticleDOI
EMD-Based Signal Filtering
TL;DR: A signal-filtering method based on empirical mode decomposition is proposed, limited to signals that were corrupted by additive white Gaussian noise, and the results are compared to well-known filtering methods.
References
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