Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing
Yong Lv,Rui Yuan,Gangbing Song +2 more
TLDR
In this paper, a multivariate empirical mode decomposition (multivariate EMD) is used to simultaneously analyze the multivariate signal to extract fault information, especially for weak fault characteristics during the period of early failure.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2016-12-15 and is currently open access. It has received 175 citations till now. The article focuses on the topics: Fault (power engineering) & Bearing (mechanical).read more
Citations
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Journal ArticleDOI
Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders.
TL;DR: The experiment results indicate that the proposed method for bearing fault diagnosis with RNN in the form of an autoencoder achieves satisfactory performance with strong robustness and high classification accuracy.
Journal ArticleDOI
Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing
TL;DR: A novel method called improved convolutional deep belief network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing and results confirm that the developed method is more effective than the traditional methods.
Journal ArticleDOI
Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions
TL;DR: A new decision fusion strategy is designed to flexibly fuse each individual target CNN to obtain the comprehensive result of the proposed ensemble transfer convolutional neural networks driven by multi-channel signals.
Journal ArticleDOI
A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery.
TL;DR: This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings and serves as a guidemap for researchers in the field of early fault diagnosis.
Journal ArticleDOI
Bolt early looseness monitoring using modified vibro-acoustic modulation by time-reversal
Furui Wang,Gangbing Song +1 more
TL;DR: A modified VAM (MVAM) is developed that can circumvent existing problems with practical implementation and provide higher sensitivity in bolt early looseness monitoring, and is compared with the proposed MMSE-based DI with nonlinear DI of traditional VAM method.
References
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Independent component analysis: algorithms and applications
Aapo Hyvärinen,Erkki Oja +1 more
TL;DR: The basic theory and applications of ICA are presented, and the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible.
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A Review of Image Denoising Algorithms, with a New One
TL;DR: A general mathematical and experimental methodology to compare and classify classical image denoising algorithms and a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image are defined.
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Empirical Wavelet Transform
TL;DR: This paper presents a new approach to build adaptive wavelets, the main idea is to extract the different modes of a signal by designing an appropriate wavelet filter bank, which leads to a new wavelet transform, called the empirical wavelets transform.
Journal ArticleDOI
Multivariate empirical mode decomposition
TL;DR: The proposed algorithm to use real-valued projections along multiple directions on hyperspheres in order to calculate the envelopes and the local mean of multivariate signals, leading to multivariate extension of EMD.
Journal ArticleDOI
Bivariate Empirical Mode Decomposition
TL;DR: The empirical mode decomposition is extended to bivariate time series that generalizes the rationale underlying the EMD to the bivariate framework and is designed to extract zero-mean rotating components.
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