Journal ArticleDOI
A roller bearing fault diagnosis method based on EMD energy entropy and ANN
Yang Yu,YuDejie,Cheng Junsheng +2 more
TLDR
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.About:
This article is published in Journal of Sound and Vibration.The article was published on 2006-06-27. It has received 481 citations till now. The article focuses on the topics: Entropy (energy dispersal) & Wavelet packet decomposition.read more
Citations
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Journal ArticleDOI
A review on empirical mode decomposition in fault diagnosis of rotating machinery
TL;DR: 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.
Journal ArticleDOI
Applications of machine learning to machine fault diagnosis: A review and roadmap
TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.
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
Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification
TL;DR: An effective and reliable deep learning method known as stacked denoising autoencoder (SDA), which is shown to be suitable for certain health state identifications for signals containing ambient noise and working condition fluctuations, is investigated.
Journal ArticleDOI
Deep Model Based Domain Adaptation for Fault Diagnosis
TL;DR: This work proposed a novel deep neural network model with domain adaptation for fault diagnosis, which can find the solution to this problem by adapting the classifier or the regression model trained in a source domain for use in a different but related target domain.
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
A new view of nonlinear water waves: the Hilbert spectrum
TL;DR: In this paper, Hilbert spectral analysis is proposed as an alternative to wavelet analysis, which provides not only a more precise definition of particular events in time-frequency space, but also more physically meaningful interpretations of the underlying dynamic processes.
Journal ArticleDOI
Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis
Jing Lin,Liangsheng Qu +1 more
TL;DR: In this paper, a denoising method based on wavelet analysis is applied to feature extraction for mechanical vibration signals, which is an advanced version of the famous soft thresholding denoizing method proposed by Donoho and Johnstone.
Journal ArticleDOI
Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals
D. Ho,Robert B. Randall +1 more
TL;DR: In this paper, bearing fault vibrations are modelled as a series of impulse responses of a single-degree-of-freedom system and the model incorporates slight random variations in the time between pulses so as to resemble actual vibration signals.
Journal ArticleDOI
Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities
TL;DR: In this paper, the authors used wavelet analysis and envelope detection (ED) to detect bearing failure in a motor-pump driven system, which can detect both periodic and non-periodic signals, allowing the machine operator to more easily detect the remaining types of bearing faults.
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Ensemble empirical mode decomposition: a noise-assisted data analysis method
Zhaohua Wu,Norden E. Huang +1 more