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
TLDR
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...read more
Citations
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Journal ArticleDOI
Natural computing for mechanical systems research: A tutorial overview
TL;DR: The current paper is intended as a tutorial overview of the basic theory of some of the most common methods of natural computing as they are applied in the context of mechanical systems research.
Journal ArticleDOI
Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions
TL;DR: It has been shown that the feature space formed using ellipse area parameters of first and second IMFs has given good classification performance and will be used for classification of ictal and seizure-free EEG signals using the artificial neural network (ANN) classifier.
Journal ArticleDOI
Planetary gearbox fault diagnosis using an adaptive stochastic resonance method
TL;DR: In this paper, an adaptive stochastic resonance (ASR) method was proposed for fault diagnosis of planetary gearboxes, which utilizes the optimization ability of ant colony algorithms and adaptively realizes the optimal stochastically resonance system matching input signals.
Journal ArticleDOI
Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics.
TL;DR: A new approach for feature extraction/selection based on trigonometric functions and cumulative transformation, and the selection is performed by evaluating feature fitness using monotonicity and trendability characteristics, which is applied to the time-frequency analysis of nonstationary signals using a discrete wavelet transform.
Journal ArticleDOI
Time-frequency representations of Lamb waves.
TL;DR: The utility of using TFRs to quantitatively resolve changes in the frequency content of these nonstationary signals, as a function of time, is illustrated.
References
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TL;DR: In this paper, the authors used the representations of the noise currents given in Section 2.8 to derive some statistical properties of I(t) and its zeros and maxima.
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