Journal ArticleDOI
A review on empirical mode decomposition in fault diagnosis of rotating machinery
Reads0
Chats0
TLDR
This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2013-02-01. It has received 1410 citations till now. The article focuses on the topics: Fault (power engineering).read more
Citations
More filters
Journal ArticleDOI
Artificial intelligence for fault diagnosis of rotating machinery: A review
TL;DR: This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications.
Journal ArticleDOI
Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals
TL;DR: In this article, a mathematical analysis to select the most significant intrinsic mode functions (IMFs) is presented, and the chosen features are used to train an artificial neural network (ANN) to classify bearing defects.
Journal ArticleDOI
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
TL;DR: Experimental results and comprehensive comparison analysis have demonstrated the superiority of the proposed MSCNN approach, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise.
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
A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
TL;DR: A novel deep autoencoder feature learning method is developed to diagnose rotating machinery fault and the results confirm that the proposed method is more effective and robust than other methods.
References
More filters
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
Empirical mode decomposition as a filter bank
TL;DR: It turns out that EMD acts essentially as a dyadic filter bank resembling those involved in wavelet decompositions, and the hierarchy of the extracted modes may be similarly exploited for getting access to the Hurst exponent.
Journal ArticleDOI
A study of the characteristics of white noise using the empirical mode decomposition method
Zhaohua Wu,Norden E. Huang +1 more
TL;DR: In this article, empirical experiments on white noise using the empirical mode decomposition (EMD) method were conducted and it was shown empirically that the EMD is effectively a dyadic filter, the intrinsic mode function (IMF) components are all normally distributed, and the Fourier spectra of the IMF components cover the same area on a semi-logarithmic period scale.
Journal ArticleDOI
A review on Hilbert‐Huang transform: Method and its applications to geophysical studies
Norden E. Huang,Zhaohua Wu +1 more
TL;DR: Hilbert-Huang transform, consisting of empirical mode decomposition and Hilbert spectral analysis, is a newly developed adaptive data analysis method, which has been used extensively in geophysical research.
Related Papers (5)
Ensemble empirical mode decomposition: a noise-assisted data analysis method
Zhaohua Wu,Norden E. Huang +1 more