A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing

Peng, Z.K.; Tse, Peter W.; Chu, Fulei

doi:10.1016/j.ymssp.2004.01.006

Cited by 708 publications

(407 citation statements)

References 16 publications

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“…The concept of multilevel filtering has been applied in the EMD domain [38] and in the flexible analytic wavelet transform (FAWT) [39] domain [40]. However, in some cases, the EMD method failed to extract low-energy components from the analyzed time series [41], and hence, low-energy components are absent in the time-frequency plane. Secondly, the EMD method may not be suitable for signals consisting of singularities and localized waves [42].…”

Section: Introductionmentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

et al. 2017

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This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high-and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

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Section: Introductionmentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

et al. 2017

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“…The WT decomposition subseries representation of a signal also consists of an a priori, predefined set of approximations and details (wavelet is selected first) [26]. That is, neither the FT nor WT are able to show the instantaneous frequency and intrinsic behavior of a signal [27]. However, the HHT is able to determine the instantaneous frequency with high accuracy, and hence its decomposition is more accurate and representative.…”

Section: Hilbert-huang Transform (Hht)mentioning

confidence: 99%

Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

et al. 2017

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Abstract:The power generated by wind generators is usually associated with uncertainties, due to the intermittency of wind speed and other weather variables. This creates a big challenge for transmission system operators (TSOs) and distribution system operators (DSOs) in terms of connecting, controlling and managing power networks with high-penetration wind energy. Hence, in these power networks, accurate wind power forecasts are essential for their reliable and efficient operation. They support TSOs and DSOs in enhancing the control and management of the power network. In this paper, a novel two-stage hybrid approach based on the combination of the Hilbert-Huang transform (HHT), genetic algorithm (GA) and artificial neural network (ANN) is proposed for day-ahead wind power forecasting. The approach is composed of two stages. The first stage utilizes numerical weather prediction (NWP) meteorological information to predict wind speed at the exact site of the wind farm. The second stage maps actual wind speed vs. power characteristics recorded by SCADA. Then, the wind speed forecast in the first stage for the future day is fed to the second stage to predict the future day's wind power. Comparative selection of input-data parameter sets for the forecasting model and impact analysis of input-data dependency on forecasting accuracy have also been studied. The proposed approach achieves significant forecasting accuracy improvement compared with three other artificial intelligence-based forecasting approaches and a benchmark model using the smart persistence method.

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“…The cause of this behavior is the inherent problem in the spline fitting. This problem can be avoided by proper selection of IMFs [9].…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

“…Fast Fourier transform is not efficient as the signal is amplitude modulated and FFT is meant for linear and stationary signals [8]. Wavelet Transform is designed for linear signals only and they have the leakage problem due to limited length of the window [9]. Another drawback of WT is that it uses decomposition scale for analysis and does not take the signal characteristics into consideration.…”

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confidence: 99%

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Morlet Wavelet UDWT Denoising and EMD based Bearing Fault Diagnosis

Raj¹,

Muthialu²

2013

ELS

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A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing

Cited by 708 publications

References 16 publications

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

Morlet Wavelet UDWT Denoising and EMD based Bearing Fault Diagnosis

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