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11-year solar cycle in the stratosphere extracted by the empirical mode decomposition method

SOLAR VARIABILITY AND CLIMATE CHANGE 2004年第2期34卷 323-329页

作者： Coughlin, KT Tung, KK Univ Washington Seattle WA 98195 USA

We apply a novel method to extract the solar cycle signal from stratospheric data. An alternative to traditional analysis is a nonlinear empirical mode decomposition (EMD) method. This method is adaptive and therefore highly efficient at identifying embedded structures, even those with small amplitudes. Using this analysis, the geopotential height in the Northern Hemisphere can be completely decomposed into five non-stationary temporal modes including an annual cycle, a QBO signal, an ENSO-like mode, a solar cycle signal and a trend. High correlations with the sunspot cycle unambiguously establish that the fourth mode is an 11-year solar cycle signal. (C) 2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： solar cycles stratosphere empirical mode decomposition method

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Vibrodiagnostics of Compressor Valves via empirical mode decomposition method 2

Vibrodiagnostics of Compressor Valves via Empirical Mode Dec...

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2nd International Ural Conference on Measurements (UralCon)

作者： Khvostov, A. A. Ryazhskih, V. I. Ryazhskih, A. V. Zhukovsky Gagarin Air Force Acad Dept Math Voronezh Russia Voronezh State Tech Univ Dept Appl Math Voronezh Russia

ISBN: (纸本)9781538605219

The paper presents a mathematical model of a piston compressor which is a part of an oxygen gas generator. Taking into account the structural parameters of the compressor, as well as the suction and pressurization valves, such model allows one to obtain the dynamics of the pressure amplitude under the compressor's piston with the vibration velocity and vibration acceleration that are transmitted in the form of mechanical affections to the equipment (such mechanical affections can be observed at the corresponding sensors use). We also investigate the use of the empirical mode decomposition algorithm for determination of the compressor's model parameters that determine the defect evolution.

关键词： vibrodiagnostics piston compressors oxygen gas generator empirical mode decomposition method

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Single-channel blind source separation for vibration signals based on TVF-EMD and improved SCA

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IET SIGNAL PROCESSING 2020年第4期14卷 259-268页

作者： Ma, Baoze Zhang, Tianqi Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China

A novel single-channel blind source separation method is studied. This method not only addresses the problem that the traditional blind source separation method depends on the number of sensors but also achieves the purpose of effectively identifying the modal parameters. Firstly, the time varying filter for empirical mode decomposition (TVF-EMD) method is used to decompose the one-dimensional observation signal into intrinsic mode functions (IMFs) with different scale characteristics. Then, the similarity measure of the probability density function between each IMF and the one-dimensional observation is calculated. According to the threshold, several sensitive IMFs are selected and a new observation is constructed with random partial reconstruction to form a two-dimensional matrix. Finally, the reconstructed signals are separated by the improved sparse component analysis (SCA) method based on energy detection in the frequency domain. The simulation results demonstrate that the proposed method can separate not only simulated vibration signal, but also damping sinusoidal signal effectively from the single-channel while the source number is unknown. And the parameters of natural frequencies and damping ratios of modal responses can be accurately identified in the test of the measured cantilever beam hammering.

关键词： blind source separation vibrations damping cantilevers time-varying filters modal parameters empirical mode decomposition method TVF-EMD one-dimensional observation signal intrinsic mode functions IMF different scale characteristics probability density function sensitive IMFs two-dimensional matrix reconstructed signals improved sparse component analysis method vibration signal sinusoidal signal source number single-channel blind source separation method traditional blind source separation method

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The joint probability distribution of runoff and sediment and its change characteristics with multi-time scales

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JOURNAL OF HYDROLOGY AND HYDROMECHANICS 2014年第3期62卷 218-225页

作者： Zhang, Jinping Ding, Zhihong You, Jinjun Zhengzhou Univ Inst Water Resources & Environm Zhengzhou 450001 Henan Province Peoples R China Haihe River Water Conservancy Commiss Tianjin 300072 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Hydrol Cycle Simulat & Regulat Beijing 100038 Peoples R China

River runoff and sediment transport are two related random hydrologic variables. The traditional statistical analysis method usually requires those two variables to be linearly correlated, and also have an identical marginal distribution. Therefore, it is difficult to know exactly the characteristics of the runoff and sediment in reality. For this reason, copulas are applied to construct the joint probability distribution of runoff and sediment in this article. The risk of synchronous-asynchronous encounter probability of annual rich-poor runoff and sediment is also studied. At last, the characteristics of annual runoff and sediment with multi-time scales in its joint probability distribution space are simulated by empirical mode decomposition method. The results show that the copula function can simulate the joint probability distribution of runoff and sediment of Huaxia hydrological station in Weihe River well, and that such joint probability distribution has very complex change characteristics at time scales.

关键词： Runoff Sediment Joint probability distribution Copula empirical mode decomposition method

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Realising the decomposition of a multi-frequency signal under the coloured noise background by the adaptive stochastic resonance in the non-linear system with periodic potential

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IET SIGNAL PROCESSING 2018年第7期12卷 930-936页

作者： Huang, Xiaogang Zhang, Jingling Lv, Meilei Shen, Gang Yang, Jianhua Shanghai Maritime Univ Dept Elect Automat Shanghai 201306 Peoples R China Quzhou Univ Coll Elect & Informat Engn Quzhou 324000 Peoples R China China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

The authors investigate a multi-frequency signal which is decomposed failure by the traditional empirical mode decomposition (EMD) method. Moreover, the multi-frequency signal submerged in the coloured noise increases the difficulty in signal decomposition. As a result, this noisy signal is decomposed unsuccessfully by the cooperation of the adaptive stochastic resonance (SR) in the classic bistable system and EMD. Then, a method combined adaptive SR in the periodic potential system and EMD is put forward to realise the decomposition. Meanwhile, the random particle swarm optimisation algorithm is applied to reach the optimal situation when signal-to-noise ratio attains the maximum value. Different simulation results verify the effectiveness of the proposed method. The proposed method might be useful in dealing with signal processing problems.

关键词： nonlinear systems stochastic processes adaptive signal processing particle swarm optimisation random processes multifrequency signal decomposition coloured noise background adaptive stochastic resonance nonlinear system periodic potential system empirical mode decomposition method EMD method classic bistable system random particle swarm optimisation algorithm signal-to-noise ratio signal processing problems

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Detection and classification of micro-grid faults based on HHT and machine learning techniques

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第2期12卷 388-397页

作者： Mishra, Manohar Rout, Pravat Kumar Siksha O Anusandhan Univ Elect Engn Dept Bhubaneswar 751030 Odisha India Siksha O Anusandhan Univ Inst Tech Educ & Res Elect & Elect Engn Dept Bhubaneswar 751030 Odisha India

This study presents a novel micro-grid protection scheme based on Hilbert-Huang transform (HHT) and machine learning techniques. Initialisation of the proposed approach is done by extracting the three-phase current signals at the targeted buses of different feeders. The obtained non-stationary signals are passed through the empirical mode decomposition method to extract different intrinsic mode functions (IMFs). In the next step using HHT to the selected IMFs component, different needful differential features are computed. The extracted features are further used as an input vector to the machine learning models to classify the fault events. The proposed micro-grid protection scheme is tested for different protection scenarios, such as the type of fault (symmetrical, asymmetrical and high impedance fault), micro-grid structure (radial and mesh) and mode of operation (islanded and grid connected) and so on. Three different machine learning models are tested and compared in this framework: Naive Bayes classifier, support vector machine and extreme learning machine. The extensive simulated results from a standard IEC micro-grid model prove the effectiveness and reliability of the proposed micro-grid protection scheme.

关键词： distributed power generation fault diagnosis learning (artificial intelligence) power generation protection Hilbert transforms decomposition feature extraction power generation faults support vector machines Bayes methods power generation reliability power engineering computing microgrid fault detection microgrid fault event classification HHT extreme machine learning technique microgrid protection scheme Hilbert-Huang transform three-phase current signal extraction empirical mode decomposition method intrinsic mode function IMF feature extraction input vector symmetrical fault asymmetrical fault high impedance fault islanding grid connection naive Bayes classifier support vector machine IEC microgrid model reliability

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A fast online bandwidth empirical mode decomposition scheme for avoidance of the mode mixing problem

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2018年第20期232卷 3652-3674页

作者： Massouleh, S. H. Momeni Kordkheili, Seyed Ali Hosseini Navazi, H. Mohammad Sharif Univ Technol Aerosp Engn Dept Ctr Res & Dev Space Sci & Technol Azadi AvePOB 11365 Tehran Iran

The main objective of this work is to propose a scheme to extract intrinsic mode functions of online data with an acceptable speed as well as accuracy. For this purpose, an individual block framework method is firstly employed to extract the intrinsic mode functions. In this method, buffers are selected such that they overlap with their neighbors to prevent the end effect errors with no need for the averaging process. And in order to avoid the mode mixing problem, a bandwidth empirical mode decomposition scheme is developed to effectively improve the results. Through this scheme, an auxiliary function made of both high- and low-frequency components corresponding to noise and dominant frequency is added to data for the strengthening of the components for the better extraction of intrinsic mode functions during sifting process. An index criterion as well as a threshold limit is also introduced to separate high- and low-frequency parts of data at desired frequency range. Advantages of the proposed scheme are assessed and comparisons with the available methods are presented. Solution of different types of examples and experimentally generated data for two faulty ball bearings reveals that the present easily implemented scheme achieves results with lower computational efforts and accuracy.

关键词： Bandwidth empirical mode decomposition method denoising empirical mode decomposition method mode mixing problem online data

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Research on the drought index of irrigation district with multi-time scales

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2013年第10期185卷 8749-8757页

作者： Zhang, Jinping Guo, Bingtuo Ding, Zhihong Zhengzhou Univ Zhengzhou 450001 Henan Province Peoples R China Yellow River Engn Consulting Co Ltd Zhengzhou 450003 Henan Province Peoples R China Tianjin Zhongshui Sci & Technol Consulting Co Ltd Tianjin 300170 Peoples R China Zhengzhou Univ Inst Water Resources & Environm Zhengzhou 450001 Henan Province Peoples R China

The empirical mode decomposition method is applied to analyze fluctuating periods and local features of the annual drought index and the drought index in the irrigation and non-irrigation periods from 1956 to 2010 in the Yinchuan irrigation district. In order to understand the uncertainty between these variables, the set pair analysis method is used to present the identity, discrepancy, and contrary of the drought index with multi-time scales. The results reveal that the annual drought index and the drought index in the irrigation and non-irrigation periods have a complex relationship which may be related to El Nio, the air-sea intersection, and the long period of solar activity. The drought index in the irrigation and non-irrigation periods presents mainly the contrary and the discrepancy;the fluctuating shapes of the annual drought index and drought index in the irrigation period are the same on their different period levels. The original annual drought index and its intrinsic mode function components have a certain connection degree;they mainly present the discrepancy.

关键词： Drought index empirical mode decomposition method Set pair analysis method Multi-time scales

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Hard component detection of transient noise and its removal using empirical mode decomposition and wavelet-based predictive filter

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IET SIGNAL PROCESSING 2018年第7期12卷 907-916页

作者： Tanwar, Pushpraj Somkuwar, Ajay Maulana Azad Natl Inst Technol Dept Elect & Commun Engn Bhopal India

In this study, the authors propose a novel method that provides weapon sound (WS) interference reduction of military instruction sound or instructions sound (IS). This method comprises of the following steps. In the first step, the mixed signal is split into its basic constituents using principal component analysis. The second step provides the intrinsic mode functions using the empirical mode decomposition method. In the third step, the fundamental frequency component is extracted by cepstrum analysis. The localisation of WS is done using the grid search window-dominant signal subspace-based method in the fourth step. Multiscale prediction and filtering using Daubechies wavelet-based prediction are applied in the final step. The proposed method provides better results than existing baseline methods, for various signal-to-noise ratios. The simulations have also been performed on recorded WS corrupted IS signals from three different distances for the various input signal-to-weapon noise ratios, to verify the accuracy of the proposed method.

关键词： military communication weapons signal detection filtering theory principal component analysis wavelet transforms cepstral analysis interference suppression interference (signal) hard component detection transient noise empirical mode decomposition method wavelet-based predictive filter weapon sound interference reduction military instruction sound military instructions sound principal component analysis intrinsic mode functions fundamental frequency component cepstrum analysis WS localisation grid search window-dominant signal subspace-based method multiscale prediction Daubechies wavelet-based prediction signal-to-noise ratios recorded WS corrupted IS signals input signal-to-weapon noise ratios

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Dynamic and quasi-static signal separation method for bridges under moving loads based on long-gauge FBG strain monitoring

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2019年第2期38卷 388-402页

作者： Lu, Huaxi Gao, Zhicheng Wu, Bitao Zhou, Zhenwei East China Jiaotong Univ Coll Civil Engn Nanchang 330013 Jiangxi Peoples R China Southeast Univ Coll Civil Engn Nanjing Jiangsu Peoples R China

Structural health monitoring is an important means of obtaining the state information of bridges, and the extracted quasi-static strain signal can reflect the stress state of bridges directly. However, the strain signals acquired during the operation stage of bridges are dynamic, and the strain gauges used in the health monitoring system are short (no more than 10 cm), which means they are easily affected by small damage at the installation parts of bridges and thereby the monitoring signal abnormalities occur. A type of externally affixed long-gauge fiber strain gauge is used to monitor the health of bridges, and the dynamic and quasi-static signal separation method for long-gauge strain sensors is studied under different vehicle loads;at the same time, the dynamic monitoring performance of the long-gauge sensor is investigated in this paper. The quasi-static strain signal extracted from the dynamic macro-strain signal can be used to directly monitor the stress status of the bridge. The results show that the method proposed in this paper is feasible for extracting the quasi-static macro-strain from a dynamic long-gauge strain signal.

关键词： Bridge health monitoring signal processing bridge engineering macro-strain empirical mode decomposition method

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