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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Theoretical analysis of the second-order synchrosqueezing transform
TL;DR: In this paper, a theoretical analysis of the synchrosqueezing transform adapted to multicomponent signals made of strongly frequency modulated modes was presented, which was recently proposed in the short time Fourier transform framework.
Journal ArticleDOI
Simulation-driven machine learning: Bearing fault classification
TL;DR: Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping to bearing fault classification is proposed as a robust, parameter free method for race fault detection.
Journal ArticleDOI
Specific Emitter Identification via Hilbert–Huang Transform in Single-Hop and Relaying Scenarios
TL;DR: This paper investigates the specific emitter identification (SEI) problem, which distinguishes different emitters using features generated by the nonlinearity of the power amplifiers of emitters, and three algorithms based on the Hilbert spectrum are proposed that show effectiveness in both single-hop and relaying scenarios, as well as under different channel conditions.
Journal ArticleDOI
An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter
Manas Rakshit,Susmita Das +1 more
TL;DR: Qualitative and quantitative study and analysis indicate that the proposed technique can be used as an effective tool for denoising of ECG signals and hence can serve for better diagnostic in computer-based automated medical system.
Journal ArticleDOI
EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis
TL;DR: In this article, a multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role.
References
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Journal ArticleDOI
Deterministic nonperiodic flow
TL;DR: In this paper, it was shown that nonperiodic solutions are ordinarily unstable with respect to small modifications, so that slightly differing initial states can evolve into considerably different states, and systems with bounded solutions are shown to possess bounded numerical solutions.
Book
Linear and Nonlinear Waves
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.
Book
RANDOM DATA Analysis and Measurement Procedures
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.
Journal ArticleDOI
Mathematical analysis of random noise
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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