Journal ArticleDOI
Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform
TLDR
In this paper, an ensemble empirical mode decomposition (EEMD) is applied on the low Q-factor transient impact component and satisfactory extraction result is obtained, and the original signal of rolling bearing early weak fault is decomposed by EEMD and several intrinsic mode functions (IMFs) are obtained.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2014-10-03. It has received 214 citations till now. The article focuses on the topics: Wavelet transform & Hilbert–Huang transform.read more
Citations
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Journal ArticleDOI
A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings
Akhand Rai,S. H. Upadhyay +1 more
TL;DR: In this article, the authors have presented the various signal processing methods applied to the fault diagnosis of rolling element bearings with the objective of giving an opportunity to the examiners to decide and select the best possible signal analysis method as well as the excellent defect representative features for future application in the prognostic approaches.
Journal ArticleDOI
Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings
TL;DR: Results show that the proposed method outperforms EMD-AMma, ensemble empirical mode decomposition-AMMA, and generalized empirical mode decompposition-empirical envelope demodulation in detecting early inner race fault.
Journal ArticleDOI
A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing
TL;DR: The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outerRace fault, and rolling element fault of the motor bearing.
Journal ArticleDOI
Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform
TL;DR: In this paper, an ensemble super-wavelet transform (ESW) is proposed for investigating vibration features of motor bearing faults, which is based on the combination of tunable Q-factor wavelet transform and Hilbert transform.
Journal ArticleDOI
EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis
TL;DR: In this article, a multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role.
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
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
Fast computation of the kurtogram for the detection of transient faults
TL;DR: This communication describes a fast algorithm for computing the kurtogram over a grid that finely samples the ( f, Δ f ) plane and the efficiency of the algorithm is illustrated on several industrial cases concerned with the detection of incipient transient faults.
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Ensemble empirical mode decomposition: a noise-assisted data analysis method
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