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Journal ArticleDOI

A review on empirical mode decomposition in fault diagnosis of rotating machinery

01 Feb 2013-Mechanical Systems and Signal Processing (Academic Press)-Vol. 35, Iss: 1, pp 108-126
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.
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).
Citations
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Journal ArticleDOI
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.

1,287 citations

Journal ArticleDOI
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.

594 citations

Journal ArticleDOI
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.
Abstract: This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches, where feature extraction and classification are separately designed and performed, this paper aims to automatically learn effective fault features directly from raw vibration signals while classify the type of faults in a single framework, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise. Considering the multiscale characteristics inherent in vibration signals of a gearbox, a new multiscale convolutional neural network (MSCNN) architecture is proposed to perform multiscale feature extraction and classification simultaneously. The proposed MSCNN incorporates multiscale learning into the traditional CNN architecture, which has two merits: 1) high-level fault features can be effectively learned by the hierarchical learning structure with multiple pairs of convolutional and pooling layers; and 2) multiscale learning scheme can capture complementary and rich diagnosis information at different scales. This greatly improves the feature learning ability and enables better diagnosis performance. The proposed MSCNN approach is evaluated through experiments on a WT gearbox test rig. Experimental results and comprehensive comparison analysis with respect to the traditional CNN and traditional multiscale feature extractors have demonstrated the superiority of the proposed method.

532 citations


Cites methods from "A review on empirical mode decompos..."

  • ...These methods have been considered as multiscale feature extraction ones and widely used in vibration-based fault diagnosis for rotary machines [9], [10], [20]....

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  • ...WPD and EMD can decompose a complex vibration signal into several different components containing different frequency bands information....

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  • ...For the EMD, we calculate the energy vector of first six intrinsic mode functions of each sample....

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  • ...In order to demonstrate the merits of our proposed MSCNN, three traditional feature extraction methods, including multiscale entropy (MSE) [20], wavelet package decomposition (WPD) [9], and EMD [10], are compared....

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  • ...[8] J. Wang, R. X. Gao, and R. Yan, “Integration of EEMD and ICA for wind turbine gearbox diagnosis,” Wind Energy, vol. 17, no. 5, pp. 757–773, May 2014....

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Journal ArticleDOI
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.

482 citations

Journal ArticleDOI
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.

481 citations

References
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Journal ArticleDOI
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.
Abstract: A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...

18,956 citations

Journal ArticleDOI
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.
Abstract: A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. 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. With this ensemble mean, one can separate scales naturall...

6,437 citations


"A review on empirical mode decompos..." refers background or methods in this paper

  • ...Each trial consists of the decomposition results of the signal plus a white noise of finite amplitude [48]....

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  • ...To solve the problem of mode mixing in the original EMD, a noise-assisted data analysis method, namely, ensemble empirical mode decomposition (EEMD), was developed by Wu and Huang [48] by adding noise to the investigated signal....

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Journal ArticleDOI
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.
Abstract: Empirical mode decomposition (EMD) has recently been pioneered by Huang et al. for adaptively representing nonstationary signals as sums of zero-mean amplitude modulation frequency modulation components. In order to better understand the way EMD behaves in stochastic situations involving broadband noise, we report here on numerical experiments based on fractional Gaussian noise. In such a case, it turns out that EMD acts essentially as a dyadic filter bank resembling those involved in wavelet decompositions. It is also pointed out that the hierarchy of the extracted modes may be similarly exploited for getting access to the Hurst exponent.

2,304 citations


"A review on empirical mode decompos..." refers methods in this paper

  • ...EEMD To overcome the problem of mode mixing, EEMD was introduced based on the statistical properties of white noise, which showed that the EMD method is an effective self-adaptive dyadic filter bank when applied to the white noise, and the noise could help data analysis in the decomposition of EMD [49–51]....

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Journal ArticleDOI
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.
Abstract: Based on numerical experiments on white noise using the empirical mode decomposition (EMD) method, we find 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 are all identical and cover the same area on a semi–logarithmic period scale. Expanding from these empirical findings, we further deduce that the product of the energy density of IMF and its corresponding averaged period is a constant, and that the energy–density function is chi–squared distributed. Furthermore, we derive the energy–density spread function of the IMF components. Through these results, we establish a method of assigning statistical significance of information content for IMF components from any noisy data. Southern Oscillation Index data are used to illustrate the methodology developed here.

1,573 citations


"A review on empirical mode decompos..." refers methods in this paper

  • ...EEMD To overcome the problem of mode mixing, EEMD was introduced based on the statistical properties of white noise, which showed that the EMD method is an effective self-adaptive dyadic filter bank when applied to the white noise, and the noise could help data analysis in the decomposition of EMD [49–51]....

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Journal ArticleDOI
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.
Abstract: [1] Data analysis has been one of the core activities in scientific research, but limited by the availability of analysis methods in the past, data analysis was often relegated to data processing. To accommodate the variety of data generated by nonlinear and nonstationary processes in nature, the analysis method would have to be adaptive. 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. In this review, we will briefly introduce the method, list some recent developments, demonstrate the usefulness of the method, summarize some applications in various geophysical research areas, and finally, discuss the outstanding open problems. We hope this review will serve as an introduction of the method for those new to the concepts, as well as a summary of the present frontiers of its applications for experienced research scientists.

1,533 citations


"A review on empirical mode decompos..." refers background in this paper

  • ...Huang and Wu [19] provided a thorough review on EMD and Hilbert–Huang transform applied to geophysical studies, while a survey on the use of EMD to fault diagnosis of rotating machinery has not been reported based on the authors’ literature search....

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