Journal ArticleDOI
Bearing damage assessment using Jensen-Rényi Divergence based on EEMD
TLDR
In this article, an ensemble empirical mode decomposition (EEMD) and Jensen Renyi divergence (JRD) based methodology is proposed for the degradation assessment of rolling element bearings using vibration data.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2017-03-15. It has received 98 citations till now.read more
Citations
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Journal ArticleDOI
A review on data-driven fault severity assessment in rolling bearings
Mariela Cerrada,Mariela Cerrada,René-Vinicio Sánchez,Chuan Li,Fannia Pacheco,Diego Cabrera,José Valente de Oliveira,Rafael E. Vásquez +7 more
TL;DR: In this article, a review of fault severity assessment of rolling bearing components is presented, focusing on data-driven approaches such as signal processing for extracting proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions.
Journal ArticleDOI
A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders
TL;DR: The results confirm that the proposed method can get rid of the dependence on manual feature extraction and overcome the limitations of individual deep learning models, which is more effective than the existing intelligent diagnosis methods.
Journal ArticleDOI
The Entropy Algorithm and Its Variants in the Fault Diagnosis of Rotating Machinery: A Review
TL;DR: This paper aims to investigate the applications of entropy for the fault characteristics extraction of rotating machines and reviews the applications using the original entropy method and the improved entropy methods, respectively.
Journal ArticleDOI
Fault diagnosis of rotating machines based on the EMD manifold
TL;DR: This paper proposes a new noise-assisted method, called EMD manifold (EMDM), for enhanced fault diagnosis of rotating machines, by which true fault-related transients are preserved, while fault-unrelated components including mode-mixing-induced components and the residual noise derived from both the added and self-contained noise are greatly suppressed.
Journal ArticleDOI
Application of an improved minimum entropy deconvolution method for railway rolling element bearing fault diagnosis
TL;DR: The proposed improved deconvolution method for the fault detection of rolling element bearings solves the filter coefficients by the standard particle swarm optimization algorithm, assisted by a generalized spherical coordinate transformation.
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
Rolling element bearing diagnostics—A tutorial
Robert B. Randall,Jérôme Antoni +1 more
TL;DR: This tutorial is intended to guide the reader in the diagnostic analysis of acceleration signals from rolling element bearings, in particular in the presence of strong masking signals from other machine components such as gears.
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
Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics
TL;DR: In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter based denoising methods are compared based on signals from mechanical defects, and the comparison result reveals that wavelet filters are more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet transform has a better performance on smooth signal detection.
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Ensemble empirical mode decomposition: a noise-assisted data analysis method
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