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
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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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Separation of Overlapped Non-Stationary Signals by Ridge Path Regrouping and Intrinsic Chirp Component Decomposition
TL;DR: A novel non-parametric algorithm called ridge path regrouping (RPRG) is proposed to extract the instantaneous frequencies (IFs) of the overlapped components from a T-F representation (TFR).
Journal ArticleDOI
Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition
TL;DR: In this paper, a hybrid signal processing method that combines spectral kurtosis (SK) with ensemble empirical mode decomposition (EEMD) was developed to diagnose the health of bearing signals.
Journal ArticleDOI
Automatic microseismic event picking via unsupervised machine learning
TL;DR: A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseISMic events, even in the case of moderately strong background noise.
Journal ArticleDOI
A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM
TL;DR: A novel model for fault diagnosis based on empirical mode decomposition (EMD) and multi-class transductive support vector machine (TSVM) is applied to diagnose the faults of the gear reducer and the experimental results obtain a very high diagnosis accuracy.
Journal ArticleDOI
Ensemble incremental learning Random Vector Functional Link network for short-term electric load forecasting
TL;DR: A hybrid incremental learning approach composed of Discrete Wavelet Transform, Empirical Mode Decomposition and Random Vector Functional Link network is presented, which can significantly improve the forecasting performance with respect to both efficiency and accuracy.
References
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TL;DR: In this paper, a general overview of the nonlinear theory of water wave dynamics is presented, including the Wave Equation, the Wave Hierarchies, and the Variational Method of Wave Dispersion.
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TL;DR: A revised and expanded edition of this classic reference/text, covering the latest techniques for the analysis and measurement of stationary and nonstationary random data passing through physical systems, is presented in this article.
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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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