• Structural Engineering and Mechanics
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• v.39 no.6
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• pp.813-831
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• 2011

This paper presents an efficient version of Hilbert-Huang transform for nonlinear non-stationary systems analyses. An ensemble empirical mode decomposition (EEMD) is introduced to alleviate the problem of mode mixing between intrinsic mode functions (IMFs) decomposed by EMD. Yet the problem has not been fully resolved when a signal of a similar scale resides in different IMF components. Instead of using a trial and error method to select the "best" outcome generated by EEMD, a hybrid algorithm based on EEMD and EMD is proposed for multi-mode signal processing. The developed approach comprises the steps from a bandpass filter design for regrouping modes of the IMFs obtained from EEMD, to the mode extraction using EMD, and to the assessment of each mode in the marginal spectrum. A simulated two-mode signal is tested to demonstrate the efficiency and robustness of the approach, showing average relative errors all equal to 1.46% for various noise levels added to the signal. The developed approach is also applied to a real bridge structure, showing more reliable results than the pure EMD. Discussions on the mode determination are offered to explain the connection between modegrouping form on the one hand, and mode-grouping performance on the other.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.9
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• pp.675-681
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• 2014

This paper proposes a fault diagnosis system for an induction motor. This system uses empirical mode decomposition(EMD) to extract fault signatures and multi-layer perceptron(MLP) neural network to facilitate an accurate fault diagnosis. EMD can not only decompose a signal adaptively but also provide intrinsic mode functions(IMFs) containing natural oscillatory modes of the signal. However, every IMF does not represent fault signature, an IMF selection algorithm based on harmonics and their energy of each IMF is proposed. The selected IMFs are utilized for fault classification using MLP and this system shows approximately 98 % diagnosis accuracy for the fault vibration signal of the induction motor.

• Smart Structures and Systems
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• v.2 no.2
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• pp.171-188
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• 2006

This paper deals with damage detection using active Lamb waves. The wavelet transform and empirical mode decomposition methods are discussed for measuring the Lamb wave's arrival time of the group velocity. An experimental system to diagnose the damage in the composite plate is developed. A method to optimize this system is also given for practical applications of active Lamb waves, which involve optimal arrangement of the piezoelectric elements to produce single mode Lamb waves. In the paper, the single mode Lamb wave means that there exists no overlapping among different Lamb wave modes and the original Lamb wave signal with the boundary reflection signals. Based on this optimized PZT arrangement method, five damage localizations on different plates are completed and the results using wavelet transform and empirical mode decomposition methods are compared.

• Smart Structures and Systems
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• v.16 no.5
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• pp.919-935
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• 2015

The Hilbert-Huang transform (HHT) consists of empirical mode decomposition (EMD) and Hilbert spectral analysis. EMD has been successfully applied for identification of mode shapes of structures based on input-output approaches. This paper aims to extend application of EMD for output-only identification of mode shapes of linear structures. In this regard, a new simple and efficient method based on band-pass filtering and EMD is proposed. Having rather accurate estimates of modal frequencies from measured responses, the proposed method is capable to extract the corresponding mode shapes. In order to evaluate the accuracy and performance of the proposed identification method, two case studies are considered. In the first case, the performance of the method is validated through the analysis of simulated responses obtained from an analytical structural model with known dynamical properties. The low-amplitude responses recorded from the UCLA Factor Building during the 2004 Parkfield earthquake are used in the second case to identify the first three mode shapes of the building in three different directions. The results demonstrate the remarkable ability of the proposed method in correct estimation of mode shapes of the linear structures based on rather accurate modal frequencies.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.150-150
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• 2022

This study investigates the roles of decomposition methods on high accuracy in daily rainfall prediction from light gradient boosting machine (LightGBM) algorithm. Here, empirical mode decomposition (EMD) and singular spectrum analysis (SSA) methods were considered to decompose and reconstruct input time series into trend terms, fluctuating terms, and noise components. The decomposed time series from EMD and SSA methods were used as input data for LightGBM algorithm in two hybrid models, including empirical mode-based light gradient boosting machine (EMDGBM) and singular spectrum analysis-based light gradient boosting machine (SSAGBM), respectively. A total of four parameters (i.e., temperature, humidity, wind speed, and rainfall) at a daily scale from 2003 to 2017 is used as input data for daily rainfall prediction. As results from statistical performance indicators, it indicates that the SSAGBM model shows a better performance than the EMDGBM model and the original LightGBM algorithm with no decomposition methods. It represents that the accuracy of LightGBM algorithm in rainfall prediction was improved with the SSA method when using multivariate dataset.

• Journal of Mechanical Science and Technology
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• v.20 no.11
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• pp.1781-1789
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• 2006

Fourier methods are not generally an appropriate approach in the investigation of faults signals with transient components. This work presents the application of a new signal processing technique, the Hilbert-Huang transform and its marginal spectrum, in analysis of vibration signals and faults diagnosis of gear. The Empirical mode decomposition (EMD), Hilbert-Huang transform (HHT) and marginal spectrum are introduced. Firstly, the vibration signals are separated into several intrinsic mode functions (IMFs) using EMD. Then the marginal spectrum of each IMF can be obtained. According to the marginal spectrum, the wear fault of the gear can be detected and faults patterns can be identified. The results show that the proposed method may provide not only an increase in the spectral resolution but also reliability for the faults diagnosis of the gear.

• Current Optics and Photonics
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• v.2 no.5
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• pp.413-421
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• 2018

We propose a novel methodology based on the multiplication function to improve the signal-to-noise ratio (SNR) for vibration detection in a phi optical time-domain reflectometer system (phi-OTDR). The extreme-mean complementary empirical mode decomposition (ECEMD) is designed to break down the original signal into a set of inherent mode functions (IMFs). The multiplication function in terms of selected IMFs is used to determine a vibration's position. By this method, the SNR of a phi-OTDR system is enhanced by several orders of magnitude. Simulations and experiments applying the method to real data prove the validity of the proposed approach.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1450-1454
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• 2006

Empirical mode decomposition (EMD) is applied to analyze time series characterized with nonlinearity and nonstationarity. This decomposition could be utilized to construct finite and small number intrinsic mode functions (IMF) that describe complicated time series, while admitting the Hilbert transformation properties. EMD has the capability of being adaptive, capture local characteristics, and applicable to nonlinear and nonstationary processes. Unlike discrete wavelet transform (DWT), IMF eliminates spurious harmonics and retains meaningful instantaneous frequencies. Examples based on data representing natural phenomena are given to demonstrate highlight the power of this method in contrast and comparison of other ones. A presentation of the energy-frequency-time distribution of these signals found to be more informative and intuitive when based on Hilbert transformation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.6
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• pp.700-708
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• 2014

Jet engine modulation(JEM) is a frequency modulation phenomenon of the radar signal induced by electromagnetic scattering from a rotating jet engine turbine. Although JEM can be used as a representative radar target recognition method by providing unique information on the target, its recognition performance may be degraded in the observation range of weakly present JEM. Hence, this paper presents a method for extracting the JEM component by decomposing the radar signal into intrisic mode functions(IMFs) via complex empirical mode decomposition(CEMD) and by combining them based on signal eccentricity. Its application to various signals demonstrated that the proposed method improved the clarity of JEM analysis and could extend the effective observation range of JEM.

• The Magazine of the IEIE
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• v.42 no.2
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• pp.25-32
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• 2015