Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions

Ricci, R.; Pennacchi, Paolo

doi:10.1016/j.ymssp.2010.10.002

Cited by 175 publications

(88 citation statements)

References 26 publications

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“…These indicators can be used individually but are generally used in combination each other. As a matter of fact, the merit index [15] is a linear combination of the periodicity degree and absolute skewness value and the PHR [16] standing for the power-harmonic ratio employs the energy density of harmonics of both desired frequency peak and the target signal. The confidence index [18] is defined as an arithmetic between the correlation coefficient and specific indexes such as skewness, kurtosis, and impact allowance that applies the periodicity and maximum values.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Peng et al suggested an improved Hilbert-Huang transform using wavelet packet transform and applied an IMF selection based on correlation coefficients [12] and Yu et al proposed the concept of EMD energy entropy and utilized its value to identify different bearing fault types [13]. Junsheng et al exploited singular values of IMFs as fault feature vectors of support vector machines [14] and Ricci et al presented an automatic IMF selection method using a merit index [15]. Cho et al proposed an IMF selection algorithm based on power-harmonic ratio (PHR) [16] and Lei et al suggested a diagnosis method of rolling element bearings based on CEEMDAN [17].…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

et al. 2018

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Abstract:In the fault diagnosis system using empirical mode decomposition (EMD), it is important to select the intrinsic mode functions (IMFs) which contain as much fault information as possible and to alleviate the problems of mode mixing and spurious modes. An effective solution to these problems in the decomposition process can help to determine significant IMFs and to improve the performance of the fault diagnosis system. This paper describes a novel power-based IMF selection algorithm and evaluates the performance of the proposed fault diagnosis system using improved complete ensemble EMD with adaptive noise and a multi-layer perceptron neural network.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

et al. 2018

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“…Compared with FFT, HHT can deal with nonstationary and transient problems, and, compared with wavelet transform, HHT has the advantage of multiresolution, and there is no selection problem of primary function [13]. Currently, HHT analysis methods are used in many areas, like gear fault diagnosis [14,15], bearing fault diagnosis [16,17], wind power characteristics analysis [18], pneumatic conveying flow characteristics analysis [19], and so on. In the aspect of pressure fluctuation analysis and feature extraction, Feng and Chu used HHT method to analyze the pressure fluctuation signal in the draft tube during the start-up process and confirmed that the HHT method can effectively extract the low frequency and unsteady characteristics of the pressure fluctuation signal [20].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multidimensional Frequency Band Energy Ratio Analysis Method for the Pressure Fluctuation of Francis Turbine

Wang

Liang

Yue

et al. 2018

Mathematical Problems in Engineering

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The pressure fluctuation has multiple influence on the steady operation of Francis turbine, and the impact degree varies with the operation condition. In this paper, for the analysis of pressure fluctuation in the Francis turbine, a novel feature extraction method of multidimensional frequency bands energy ratio is proposed based on Hilbert Huang Transform (HHT). Firstly, the pressure fluctuation signal is decomposed into intrinsic mode functions (IMFs) by EEMD. Secondly, the Hilbert marginal spectrum is utilized to analyze the frequency characteristics of IMFs. Then, according to the inner frequency of IMFs, each of them is divided into high, medium, or low frequency band which are constructed based on the frequency characteristic of the pressure fluctuations in the Francis turbine. Afterward, the energy ratio of each frequency band to the original signal is calculated, which is to realize the feature extraction of multidimensional frequency band energy ratio. Actual applications verify that this method not only can extract the time-frequency characteristics but also can analyze the condition feature of the pressure fluctuation. It is a novel method for extracting the feature of pressure fluctuation in the Francis turbine.

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“…Recently, based on the statistical characteristics analysis of white Gaussian noise and fractional Gaussian noise in EMD sifting process [9][10][11], Flandrin et al put forward an EMD denoising scheme with partial reconstruction (EMD-PR) of relevant IMFs in an adaptive way [12], and many attempts have been made to select relevant IMFs in an efficient way [13][14][15][16][17][18][19][20]. Boudraa and Cexus proposed a distortion measure method called consecutive mean square error (CMSE) to determine the relevant IMFs [13].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Entropy EMD Thresholding for Periodic Fault Signal Enhancement in Rotating Machine

Dang

Jia

Wang

et al. 2017

Shock and Vibration

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Since the slight fault feature of incipient fault is usually polluted by heavy background noise, it is difficult to extract the weak feature signal in rotating machine. As an adaptive decomposing technique, empirical mode decomposition (EMD) based denoising methods have a good effect on the feature separation and noise elimination. However, for rotating machine with poor working environment, the components attributed to noise might have higher amplitudes, which restrict the efficiency of noise reduction in current EMD-based denoising methods. Therefore, a probabilistic entropy EMD thresholding algorithm for periodic fault signal enhancement in rotating machine is proposed in this paper. In this method, the entropy threshold of each IMF is constructed instead of the threshold applied to sampling points of each IMF directly, which overcomes the shortcoming of the denoising effect limited by larger amplitude noise reservation and smaller amplitude feature signal reduction in the current denoising methods. Meanwhile, in order to make the amplitudes of all the IMF reduce in a smooth way, a multiscale thresholding algorithm based on quantile statistics to provide probability indexes is presented. Engineering application demonstrates that the proposed method is effective in the noise reduction and fault feature enhancement in the rotating machine.

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Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions

Cited by 175 publications

References 26 publications

Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

A Novel Multidimensional Frequency Band Energy Ratio Analysis Method for the Pressure Fluctuation of Francis Turbine

Probabilistic Entropy EMD Thresholding for Periodic Fault Signal Enhancement in Rotating Machine

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