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The use of intrinsic mode functions to characterize shock and vibration in the distribution environment

PACKAGING TECHNOLOGY AND SCIENCE 2005年第1期18卷 39-51页

作者： Rouillard, V Sek, MA Victoria Univ Technol Sch Architectural Civil & Mech Engn Melbourne Vic 8001 Australia

This paper describes an innovative approach, based on the instrinsic mode functions (IMFs), to characterize the nature of mechanical vibration encountered in transport vehicles. The paper highlights the importance of understanding the nature of transport vibration and shows that their accurate characterization is essential for the optimization of protective packaging. Although there have been numerous studies aimed at characterizing random vibration during transport, the majority of those have been limited to applying relatively conventional signal analysis techniques, such as the average power spectral density (PSD) This paper investigates the benefits offered by the recently introduced Hilbert-Huang transform when characterizing non-stationary random vibration in comparison with more traditional Fourier analysis-based techniques. The paper describes the operation of the Hilbert-Huang transform, which was developed to assist in the analysis of non-Gaussian and non-stationary random data. The Hilbert-Huang transform is based on the empirical mode decomposition (EMD) technique used to produce a finite number of IMFs, which, as a set, provide a complete description of the process. It is shown how these IMFs are well suited to the application of the Hilbert-Huang transform to determine the magnitude and instantaneous frequency of each IMF. The technique is applied to various records of random vibration data collected from transport vehicles in order to illustrate the benefits of the method in characterizing the nature of non-stationarities present in transport vibration. Copyright (C) 2004 John Wiley Sons, Ltd.

关键词： vibration intrinsic mode functions power spectral density package transport Hilbert-Huang transform

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Computer cryptography through performing chaotic modulation on intrinsic mode functions with non-dyadic number of encrypted signals

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IET SIGNAL PROCESSING 2019年第1期13卷 7-13页

作者： Chen, Jinrong Ling, Bingo Wing-Kuen Feng, Peihua Lei, Ruisheng Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Guangdong Peoples R China

This study proposes a computer cryptographic system through performing the chaotic modulation on the intrinsic mode functions with a non-dyadic number of the encrypted signals. First, the empirical mode decomposition is applied to an input signal to generate a set of intrinsic mode functions. Then, these intrinsic mode functions are categorised into two groups of signals. Next, a type 1 polyphase is employed to represent each group of signals. These polyphase components are combined to generate a non-dyadic number of polyphase components. Second, the chaotic modulation is applied to these combined polyphase components for performing the encryption in the time frequency domain. To reconstruct the original signal, first, the chaotic demodulation is applied to the encrypt components to reconstruct the combined polyphase components. Then, the original groups of intrinsic mode functions are reconstructed through the type 2 polyphase representation and the original signal is reconstructed. Compared with the chaotic filter bank system, the proposed approach enjoys the nonlinear and adaptive property of the empirical mode decomposition. Therefore, a better security performance can be achieved particularly for the non-stationary signals. Compared with the conventional chaotic modulation approach, the proposed system allows performing the cryptography in the time frequency domain.

关键词： cryptography channel bank filters time-frequency analysis chaotic communication signal representation signal reconstruction Hilbert transforms chaotic modulation intrinsic mode functions nondyadic number encrypted signals empirical mode decomposition combined polyphase components nonstationary signals computer cryptography type 1 polyphase time frequency domain chaotic demodulation type 2 polyphase representation chaotic filter bank system

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Damage detection of a cable-stayed bridge based on combining effective intrinsic mode functions of empirical mode decomposition using the feature selection technique

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2021年第6期29卷 861-881页

作者： Babajanian Bisheh, Hossein Ghodrati Amiri, Gholamreza Nekooei, Masoud Darvishan, Ehsan Islamic Azad Univ Sci & Res Branch Dept Civil Engn Tehran Iran Iran Univ Sci & Technol Sch Civil Engn Nat Disasters Prevent Res Ctr Tehran Iran Islamic Azad Univ Sci & Res Branch Dept Civil Engn Tehran Iran Islamic Azad Univ Roudehen Branch Dept Civil Engn Roudehen Iran

This paper presents a novel approach for detecting damage in a cable-stayed bridge based on adaptive signal processing and feature extraction. The Empirical mode Decomposition (EMD) technique is employed in this research to obtain the intrinsic mode functions (IMFs). Several features in time and frequency domains are extracted from IMFs as damage-sensitive features. Next, to reduce false alarms, several spectral parameters are introduced as effective features. A Support Vector Machine (SVM) is employed as a classifier to compare the detection ability of the proposed and conventional features. The second part of this research employs the application of several feature selection techniques that are based on the filter, wrapper, and embedded approaches to obtain the most effective IMFs and then combine them to create a subset that carries the most significant information about the structural damage. To investigate the capability of the proposed approach, vibration acceleration data obtained from a real structure, the Yonghe bridge, is employed. Results indicate that the proposed approach can identify damage with high accuracy.

关键词： Damage identification intrinsic mode functions empirical mode decomposition feature selection cable-stayed bridge support vector machine

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Decomposed intrinsic mode functions and deep learning algorithms for water quality index forecasting

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Neural Computing and Applications 2024年第21期36卷 13223-13242页

作者： Wai, Kok Poh Koo, Chai Hoon Huang, Yuk Feng Chong, Woon Chan Department of Civil Engineering Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Jalan Sg. Long Bandar Sg. Long Cheras Selangor Kajang43000 Malaysia Department of Chemical Engineering Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Jalan Sg. Long Bandar Sg. Long Cheras Selangor Kajang43000 Malaysia

The water quality index (WQI) serves as a global representation of river water quality (WQ). Existing studies related to the WQI have mainly focused on two aspects: (i) a WQI point estimation using multiple WQ inputs;and (ii) a one-step-ahead WQI forecasting with datasets of lower temporal resolution. These approaches, however, are limited in their ability to forecast future trends of the WQI for timely and prompt responses to pollution events. In this study, the deep learning algorithms, namely the long short-term memory (LSTM) and the gated recurrent unit (GRU), were selected for direct multi-step-ahead WQI forecasting. To enhance the capability of the models in capturing their temporal patterns, the input signal was pre-decomposed using the empirical mode decomposition (EMD) and variational mode decomposition (VMD) into several intrinsic mode functions (IMFs). The characteristics of these IMFs were then analyzed and used to ease model learning on capturing their temporal patterns. Our study shows that the selection of signal decomposition strategies significantly impact the model performance. Both deep learning algorithms offered comparable performances, with the VMD-LSTM exhibiting the lowest prediction errors (MAPE = 1.9237%) and the highest Kling–Gupta efficiency (KGE = 0.6761) over a two-month test period. To the best of our knowledge, this is the first paper that applies a direct forecast approach for the multi-step-ahead WQI forecasting, using the IMFs obtained from the EMD and VMD decompositions. The performance of the model was evaluated through a rolling forward setup to ensure its consistency across different test periods. The proposed modeling framework holds the potential to assist policymakers and stakeholders in decision-making, particularly in planning remedies and efficient water resource management. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： intrinsic mode functions

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Classification of Seizure Prone EEG Signal Using Amplitude and Frequency Based Parameters of intrinsic mode functions

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JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING 2017年第4期37卷 540-553页

作者： Kaur, Manpreet Singh, Gurwinder St Longowal Inst Engn & Technol Dept EIE Longowal 148106 Punjab India St Longowal Inst Engn & Technol Dept CSE Longowal 148106 Punjab India

Epilepsy is a serious neurological disorder which disturbs the common activities of a human brain. It is detected with the help of electroencephalogram (EEG) recording of the brain as it covers detailed information related to the different physiological states of the brain. A novel method based on empirical mode decomposition (EMD) with amplitude and frequency based parameters is presented in this paper to classify the seizure activity from an EEG signal. A well-known dataset consisting of EEG signals of healthy volunteers and epileptic patients, under different stages, has been used in this study. EMD has been employed for the decomposition of an EEG signal into a set of intrinsic mode functions (IMFs). First four IMFs have been considered in this work. Two amplitude based parameters and one frequency based parameter have been extracted from each of the four IMFs. The validation of the extracted parameters of all IMFs has been carried out by Kruskal-Wallis statistical test and box plot method. Extracted parameters qualify both the validation tests for discriminating normal, interictal and ictal EEG signals. Twelve features from each EEG signal have been fed to four different classifiers;artificial neural network (ANN), least square-support vector machine (LS-SVM), random forest and Na < ve Bayes. The performance of the study has been evaluated in two stages. In the first stage, parameters from first four IMFs have been analyzed individually, with four classifiers and in the second stage, the performance has been analyzed by considering the parameters of all the four IMFs. The results of the proposed method have been compared with the other state of the art techniques for the validation.

关键词： Epilepsy Electroencephalogram Empirical mode decomposition intrinsic mode functions Artificial neural network Least square-support vector machine Random forest Naive Bayes

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Machine fault feature extraction based on intrinsic mode functions

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MEASUREMENT SCIENCE AND TECHNOLOGY 2008年第4期19卷 45105-1-45105-12页

作者： Fan, Xianfeng Zuo, Ming J. Univ Elect Sci & Technol China Sch Mechatron Engn Chengdu 610054 Sichuan Peoples R China Univ Alberta Dept Mech Engn Edmonton AB T6G 2G8 Canada

This work employs empirical mode decomposition (EMD) to decompose raw vibration signals into intrinsic mode functions (IMFs) that represent the oscillatory modes generated by the components that make up the mechanical systems generating the vibration signals. The motivation here is to develop vibration signal analysis programs that are self-adaptive and that can detect machine faults at the earliest onset of deterioration. The change in velocity of the amplitude of some IMFs over a particular unit time will increase when the vibration is stimulated by a component fault. Therefore, the amplitude acceleration energy in the intrinsic mode functions is proposed as an indicator of the impulsive features that are often associated with mechanical component faults. The periodicity of the amplitude acceleration energy for each IMF is extracted by spectrum analysis. A spectrum amplitude index is introduced as a method to select the optimal result. A comparison study of the method proposed here and some well-established techniques for detecting machinery faults is conducted through the analysis of both gear and bearing vibration signals. The results indicate that the proposed method has superior capability to extract machine fault features from vibration signals.

关键词： machine fault feature extraction intrinsic mode functions amplitude acceleration energy empirical mode decomposition

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Data-driven atomic decomposition via frequency extraction of intrinsic mode functions

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GEM-INTERNATIONAL JOURNAL ON GEOMATHEMATICS 2016年第1期7卷 117-146页

作者： Chui, Charles K. Mhaskar, H. N. van der Walt, Maria D. Stanford Univ Dept Stat Stanford CA 94305 USA CALTECH Dept Math Pasadena CA 91125 USA Claremont Grad Univ Inst Math Sci Claremont CA 91711 USA Vanderbilt Univ Dept Math Nashville TN 37240 USA

Decomposition of functions in terms of their primary building blocks is one of the most fundamental problems in mathematical analysis and its applications. Indeed, atomic decomposition of functions in the Hardy space H p for 0 < p = 1 as infinite series of "atoms" that have the property of vanishing moments with order at least up to 1/p has significant impacts, not only to the advances of harmonic and functional analyses, but also to the birth of wavelet analysis, which in turn allows the construction of sufficiently large dictionaries of wavelet-like basis functions for the success of atomic decomposition of more general functions or signals, by such mathematical tools as "basis pursuit" and "nonlinear basis pursuit". However, such dictionaries are necessarily huge for atomic decomposition of real-world signals. The spirit of the present paper is to construct the atoms directly from the data, without relying on a large dictionary. Following Gabor, the starting point of this line of thought is to observe that " any" signal a can be written as a(t) = A(t) cos f(t) via complex extension using theHilbert transform. Hence, if a given signal f has been decomposed by whatever available methods or schemes, as the sum of sub-signals f(k), then each sub-signal can be written as f(k)(t) = A(k)(t) cos phi(k)(t). Whether or not f(k) is an atom of the given signal f depends on whether any of the sub-signals f(k) can be further decomposed in a meaningful way. In this regard, the most popular decomposition scheme in the current literature is the sifting process of the empirical mode decomposition (EMD), where the sub-signals f(k) are called intrinsic mode functions (IMF's). The main contribution of our present paper is firstly to demonstrate that IMF's may not be atoms, and secondly to give a computational scheme for decomposing such IMF's into finer and meaningful signal building blocks. Our innovation is to apply the signal separation operator (SSO), introduced by the first two

关键词： Atomic decomposition Dictionaries for basis pursuit Data-driven atomic decomposition Local sifting process Instantaneous frequencies Empirical mode decomposition (EMD) Synchrosqueezing transform (SST) Hilbert transform intrinsic mode functions Signal separation operator (SSO) Super-resolution SuperEMD

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Fault diagnosis of wind turbine power converter using intrinsic mode functions with relative energy entropy

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CIRCUIT WORLD 2024年第1期50卷 90-107页

作者： Manikandan, R. Singh, R. Raja Vellore Inst Technol VIT Sch Elect Engn Vellore India Vellore Inst Technol VIT Dept Energy & Power Elect Vellore India

PurposeThe purpose of this paper is to prevent the destruction of other parts of a wind energy conversion system because of faults, the diagnosis of insulated-gate bipolar transistor (IGBT) faults has become an essential topic of study. Demand for sustainable energy sources has been prompted by rising environmental pollution and energy requirements. Renewable energy has been identified as a viable substitute for conventional fossil fuel energy generation. Because of its rapid installation time and adaptable expenditure for construction scale, wind energy has emerged as a great energy resource. Power converter failure is particularly significant for the reliable operation of wind power conversion systems because it not only has a high yearly fault rate but also a prolonged downtime. The power converters will continue to operate even after the failure, especially the open-circuit fault, endangering their other parts and impairing their functionality. Design/methodology/approachThe most widely used signal processing methods for locating open-switch faults in power devices are the short-time Fourier transform and wavelet transform (WT) - based on time-frequency analysis. To increase their effectiveness, these methods necessitate the intensive use of computational resources. This study suggests a fault detection technique using empirical mode decomposition (EMD) that examines the phase currents from a power inverter. Furthermore, the intrinsic mode function's relative energy entropy (REE) and simple logical operations are used to locate IGBT open switch failures. FindingsThe presented scheme successfully locates and detects 21 various classes of IGBT faults that could arise in a two-level three-phase voltage source inverter (VSI). To verify the efficacy of the proposed fault diagnosis (FD) scheme, the test is performed under various operating conditions of the power converter and induction motor load. The proposed method outperforms existing FD schemes in the literature

关键词： Voltage source inverter Insulated-gate bipolar transistor (IGBT) Open-switch fault Three-phase current intrinsic mode functions Time-frequency analysis

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Empirical mode Decomposition for Signal Preprocessing and Classification of intrinsic mode functions

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Pattern Recognition and Image Analysis 2018年第1期28卷 122-132页

作者： Klionskiy, D.M. Kaplun, D.I. Geppener, V.V. Computer Science Department Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

Empirical mode decomposition (EMD) is an adaptive, data-driven technique for processing and analyzing various types of non-stationary signals. EMD is a powerful and effective tool for signal preprocessing (denoising, detrending, regularity estimation) and time-frequency analysis. This paper discusses pattern discovery in signals via EMD. New approaches to this problem are introduced, which involve well-known information criteria along with some other proposed ones, which have been investigated and developed for our particular tasks. In addition, the methods expounded in the paper may be considered as a way of denoising and coping with the redundancy problem of EMD. A general classification of intrinsic mode functions (IMFs, empirical modes) in accordance with their physical interpretation is offered and an attempt is made to perform classification on the basis of the regression theory, special classification statistics and some cluster- analysis algorithm. The main advantage of the innovations is their capability of working automatically. Simulation studies have been undertaken on multiharmonic signals. We also cover some aspects of hardware implementation of EMD. © 2018, Pleiades Publishing, Ltd.

关键词： intrinsic mode functions

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A CRITERION FOR SELECTING RELEVANT intrinsic mode functions IN EMPIRICAL mode DECOMPOSITION

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ADVANCES IN DATA SCIENCE AND ADAPTIVE ANALYSIS 2010年第1期2卷 1-24页

作者： Ayenu-Prah, Albert Attoh-Okine, Nii Univ Delaware Dept Civil & Environm Engn 301 DuPont Hall Newark DE 19716 USA

Information extraction from time series has traditionally been done with Fourier analysis, which use stationary sines and cosines as basis functions. However, data that come from most natural phenomena are mostly nonstationary. A totally adaptive alternative method has been developed called the Hilbert-Huang transform (HHT), which involves generating basis functions called the intrinsic mode functions (IMFs) via the empirical mode decomposition (EMD). The EMD is a numerical procedure that is prone to numerical errors that may persist in the decomposition as extra IMFs. In this study, results of numerical experiments are presented, which would establish a stringent threshold by which relevant IMFs are distinguished from IMFs that may have been generated by numerical errors. The threshold is dependent on the correlation coefficient between the IMFs and the original signal. Finally, the threshold is applied to IMFs of earthquake signals from five accelerometers located in a building.

关键词： Empirical mode decomposition Hilbert-Huang transform intrinsic mode functions correlation coefficient Fourier analysis nonstationary data nonlinear system

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