Damage Identification in a Parabolic Arch via Orthogonal Empirical Mode Decomposition

2014 ◽  
Author(s):  
Francesco Romeo ◽  
Egidio Lofrano ◽  
Achille Paolone
Keyword(s):  
Empirical Mode Decomposition ◽  
Damage Identification ◽  
Damage Index ◽  
Experimental Tests ◽  
Processing Technique ◽  
Free Vibrations ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Parabolic Arch

Localized damage in a parabolic arch is identified by combining a time-frequency vibration signals processing technique and modal-based properties. The approach is devised to detect and locate damaged areas in the structure from its free vibrations and relies on the comparison between information gathered for the undamaged and damaged states. The non-stationary acceleration data are decomposed by applying the orthogonal empirical mode decomposition to derive the orthogonal intrinsic mode functions. The latter provide a multi-frequency basis enabling to build “pseudo-modes” based on which a new damage index is proposed. The performance of the devised approach is shown through pseudo-experimental tests on a damaged arch.

A pseudo-modal structural damage index based on orthogonal empirical mode decomposition

2019 ◽  
Vol 233 (23-24) ◽  
pp. 7545-7564 ◽  
Author(s):  
Egidio Lofrano ◽  
Francesco Romeo ◽  
Achille Paolone
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Degrees Of Freedom ◽  
Damage Identification ◽  
Damage Index ◽  
Experimental Tests ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Identification Technique ◽  
Output Only

A structural damage identification technique hinged on the combination of orthogonal empirical mode decomposition and modal analysis is proposed. The output-only technique is based on the comparison between pre- and post-damage free structural vibrations signals. The latter are either kinematic (displacements, velocities or accelerations) or deformation measures (strains or curvatures). The response data are decomposed by means of the orthogonal empirical mode decomposition to derive a finite set of orthogonal intrinsic mode functions; the latter are used as a multi-frequency and data-driven basis to build pseudo-modal shapes. A new damage index, the so-called pseudo-mode index, is introduced to compare the response obtained for the two states of the structural system and detect potential damages. The performance of the devised index in detecting a localised damage is shown through numerical and experimental tests on two structural models, namely a 4-degrees-of-freedom system and a two-hinged parabolic arch.

Spatiotemporal Trend and Variability of Precipitation in Taiwan Based on Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD)

2021 ◽  
Author(s):  
Chun-Hsiang Tang ◽  
Christina W. Tsai
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Time Frequency ◽  
Spatial Locality ◽  
Temperature And Precipitation ◽  
Mode Decomposition ◽  
Climatic Scenarios

<p>Abstract</p><p>Most of the time series in nature are nonlinear and nonstationary affected by climate change particularly. It is inevitable that Taiwan has also experienced frequent drought events in recent years. However, drought events are natural disasters with no clear warnings and their influences are cumulative. The difficulty of detecting and analyzing the drought phenomenon remains. To deal with the above-mentioned problem, Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) is introduced to analyze the temperature and rainfall data from 1975~2018 in this study, which is a powerful method developed for the time-frequency analysis of nonlinear, nonstationary time series. This method can not only analyze the spatial locality and temporal locality of signals but also decompose the multiple-dimensional time series into several Intrinsic Mode Functions (IMFs). By the set of IMFs, the meaningful instantaneous frequency and the trend of the signals can be observed. Considering stochastic and deterministic influences, to enhance the accuracy this study also reconstruct IMFs into two components, stochastic and deterministic, by the coefficient of auto-correlation.</p><p>In this study, the influences of temperature and precipitation on the drought events will be discussed. Furthermore, to decrease the significant impact of drought events, this study also attempts to forecast the occurrences of drought events in the short-term via the Artificial Neural Network technique. And, based on the CMIP5 model, this study also investigates the trend and variability of drought events and warming in different climatic scenarios.</p><p> </p><p>Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought</p>

Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Synthetic Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

scholarly journals A Highly Effective Data Preprocessing in Side-Channel Attack Using Empirical Mode Decomposition

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
ShuaiWei Zhang ◽  
XiaoYuan Yang ◽  
Lin Chen ◽  
Weidong Zhong
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Data Preprocessing ◽  
Side Channel ◽  
Intrinsic Mode Functions ◽  
Side Channel Attack ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Highly Effective ◽  
Preprocessing Technique

Side-channel attacks on cryptographic chips in embedded systems have been attracting considerable interest from the field of information security in recent years. Many research studies have contributed to improve the side-channel attack efficiency, in which most of the works assume the noise of the encryption signal has a linear stable Gaussian distribution. However, their performances of noise reduction were moderate. Thus, in this paper, we describe a highly effective data-preprocessing technique for noise reduction based on empirical mode decomposition (EMD) and demonstrate its application for a side-channel attack. EMD is a time-frequency analysis method for nonlinear unstable signal processing, which requires no prior knowledge about the cryptographic chip. During the procedure of data preprocessing, the collected traces will be self-adaptably decomposed into sum of several intrinsic mode functions (IMF) based on their own characteristics. And then, meaningful IMF will be reorganized to reduce its noise and increase the efficiency of key recovering through correlation power analysis attack. This technique decreases the total number of traces for key recovering by 17.7%, compared to traditional attack methods, which is verified by attack efficiency analysis of the SM4 block cipher algorithm on the FPGA power consumption analysis platform.

scholarly journals Application of ensemble empirical mode decomposition based on machine learning methodologies in forecasting monthly pan evaporation

2018 ◽  
Vol 50 (2) ◽  
pp. 498-516 ◽  
Author(s):  
Mohammad Rezaie-Balf ◽  
Ozgur Kisi ◽  
Lloyd H. C. Chua
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Support Vector ◽  
Pan Evaporation ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Testing Stage ◽  
Adaptive Time ◽  
Two Hybrid

Abstract Accurate prediction of pan evaporation (PE) is one of the crucial factors in water resources management and planning in agriculture. In this research, two hybrid models, self-adaptive time-frequency methodology, ensemble empirical mode decomposition (EEMD) coupled with support vector machine (EEMD-SVM) and EEMD model tree (EEMD-MT), were employed to forecast monthly PE. The EEMD-SVM and EEMD-MT were compared with single SVM and MT models in forecasting monthly PE, measured between 1975 and 2008, at Siirt and Diyarbakir stations in Turkey. The results were evaluated using four assessment criteria, Nash–Sutcliffe Efficiency (NSE), root mean square error (RMSE), performance index (PI), Willmott's index (WI), and Legates–McCabe's index (LMI). The EEMD-MT model respectively improved the accuracy of MT by 36 and 44.7% with respect to NSE and WI in the testing stage for the Siirt station. For the Diyarbakir station, the improvements in results were less spectacular, with improvements in NSE (1.7%) and WI (2.2%), respectively, in the testing stage. The overall results indicate that the proposed pre-processing technique is very promising for complex time series forecasting and further studies incorporating this technique are recommended.

scholarly journals A Reservoir Information Extraction Method Based on Improved Hilbert-Huang Transform

2019 ◽  
pp. 85-90
Author(s):  
Qingmi Yang
Keyword(s):  
Signal Processing ◽  
Seismic Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Intrinsic Mode Functions ◽  
Signal Processing Technique ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Non Stationary Signal

Hilbert-Huang transform (HHT) is a nonlinear non-stationary signal processing technique, which is more effective than traditional time-frequency analysis methods in complex seismic signal processing. However, this method has problems such as modal aliasing and end effect. The problem causes the accuracy of signal processing to drop. Therefore, this paper introduces the method of combining the Ensemble Empirical Mode Decomposition (EEMD) and the Normalized Hilbert transform (NHT) to extract the instantaneous properties. The specific process is as follows: First, the EEMD method is used to decompose the seismic signal to a series of Intrinsic Mode Functions (IMF), and then The IMFs is screened by using the relevant properties, and finally the NHT is performed on the IMF to obtain the instantaneous properties.

scholarly journals Target Recognition of SAR Images Based on Complex Bidimensional Empirical Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xinying Miao ◽  
Yunlong Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Target Recognition ◽  
Test Sample ◽  
Operating Conditions ◽  
Sar Image ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Standard Operating Condition ◽  
Bidimensional Empirical Mode Decomposition

A target recognition method for synthetic aperture radar (SAR) image based on complex bidimensional empirical mode decomposition (C-BEMD) is proposed. C-BEMD is used to decompose the original SAR image to obtain multilevel complex bidimensional intrinsic mode functions (BIMF), which reflect the two-dimensional time-frequency characteristics of the target. In the classification stage, the decomposed multilevel BIMFs are represented using the multitask sparse representation. Finally, the target category of the test sample is determined according to the reconstruction errors related to different training classes. In the experiment, the standard operating condition (SOC) and extended operating conditions (EOC) are designed based on the MSTAR dataset to test and verify the proposed method. The results confirm the effectiveness and robustness of the method.

Ensemble Noise-Reconstructed Empirical Mode Decomposition for Mechanical Fault Detection

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Jing Yuan ◽  
Zhengjia He ◽  
Jun Ni ◽  
Adam John Brzezinski ◽  
Yanyang Zi
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Measured Signal ◽  
Accelerometer Data ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Mixing Problem ◽  
Emd Method ◽  
Mode Mixing

Various faults inevitably occur in mechanical systems and may result in unexpected failures. Hence, fault detection is critical to reduce unscheduled downtime and costly breakdowns. Empirical mode decomposition (EMD) is an adaptive time-frequency domain signal processing method, potentially suitable for nonstationary and/or nonlinear processes. However, the EMD method suffers from several problems such as mode mixing, defined as intrinsic mode functions (IMFs) with incorrect scales. In this paper, an ensemble noise-reconstructed EMD method is proposed to ameliorate the mode mixing problem and denoise IMFs for enhancing fault signatures. The proposed method defines the IMF components as an ensemble mean of EMD trials, where each trial is obtained by sifting signals that have been reconstructed using the estimated noise present in the measured signal. Unlike traditional denoising methods, the noise inherent in the input data is reconstructed and used to reduce the background noise. Furthermore, the reconstructed noise helps to project different scales of the signal onto their corresponding IMFs, instrumental in alleviating the mode mixing problem. Two critical issues concerned in the method, i.e., the noise estimation strategy and the number of EMD trials required for denoising are discussed. Furthermore, a comprehensive noise-assisted EMD method is proposed, which includes the proposed method and ensemble EMD (EEMD). Numerical simulations and experimental case studies on accelerometer data collected from an industrial shaving process are used to demonstrate and validate the proposed method. Results show that the proposed method can both detect impending faults and isolate multiple faults. Hence, the proposed method can act as a promising tool for mechanical fault detection.

Rolling Bearing Fault Degree Recognition Based on Ensemble Empirical Mode Decomposition and Support Vector Regression

2013 ◽  
Vol 333-335 ◽  
pp. 550-554 ◽  
Author(s):  
Chang Qing Shen ◽  
Fei Hu ◽  
Zhong Kui Zhu ◽  
Fan Rang Kong
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Regression ◽  
Empirical Mode Decomposition ◽  
Economic Loss ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Bearing Fault ◽  
Mode Decomposition

The research in bearing fault diagnosis has been attracting great attention in the past decades. Development of feasible fault diagnosis procedures to prevent failures that could cause huge economic loss timely is necessary. The whole life of the bearing is also a developing process for some sensitive features related to the fault trend. In this paper, a new scheme based on ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) to conduct bearing fault degree recognition is proposed. This analysis first extracts the sensitive features from the intrinsic mode functions (IMFs) produced by EEMD which is a potential time-frequency analysis method, and then constructs an intelligent nonlinear model with input feature vectors extracted from the IMFs and defect size as output. Through validation of experimental data, the results indicated that the bearing fault degree could be effectively and precisely recognized.

Detection of Rail Fastener Conditions Using Time-Frequency Entropy Based on Orthogonal Empirical Mode Decomposition

2013 ◽  
Vol 333-335 ◽  
pp. 1708-1712 ◽  
Author(s):  
Chong Liu ◽  
Li Hua Zhang ◽  
Tong Qun Ren ◽  
Jun Sheng Liang ◽  
Da Zhi Wang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Spectrum ◽  
Vibration Signal ◽  
Vertical Vibration ◽  
Intrinsic Mode Functions ◽  
Acceleration Response ◽  
Vibration Acceleration ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Outdoor Experiment

A method based on OEMD (Orthogonal Empirical Mode Decomposition) and the theory of time-frequency entropy was applied to detect different rail fastener conditions. The original vertical vibration acceleration response of rail under different fastening conditions was obtained from outdoor experiment. The OEMD method was used to get orthogonal IMFs (Intrinsic Mode Functions) of the original vibration signal. The Hilbert time-frequency spectrum was then obtained based on the orthogonal IMFs and corresponding entropy was calculated and compared. The results show that the method is available to detect different rail fastener conditions.

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