scholarly journals Time Domain Strain/Stress Reconstruction Based on Empirical Mode Decomposition: Numerical Study and Experimental Validation

Sensors ◽  
2016 ◽  
Vol 16 (8) ◽  
pp. 1290 ◽  
Author(s):  
Jingjing He ◽  
Yibin Zhou ◽  
Xuefei Guan ◽  
Wei Zhang ◽  
Weifang Zhang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Time Domain ◽  
Experimental Validation ◽  
Numerical Study ◽  
Mode Decomposition ◽  
Strain Stress

scholarly journals Time Domain Strain/Stress Reconstruction Based on Empirical Mode Decomposition: Numerical Study and Experimental Validation

2016 ◽  
Author(s):  
Jingjing He ◽  
Yibin Zhou ◽  
Xuefei Guan ◽  
Wei Zhang ◽  
Wei Fang Zhang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Stress Responses ◽  
Numerical Study ◽  
Reconstruction Method ◽  
Fiber Sensors ◽  
Mode Decomposition ◽  
Structural Responses ◽  
Novel Method ◽  
Strain Stress ◽  
Increasing Demand

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

scholarly journals sEMG based Classification of Hand Gestures using EMD Algorithm and its Application on Control System

2019 ◽  
Vol 8 (11S) ◽  
pp. 1103-1105
Keyword(s):  
Support Vector Machine ◽  
Control System ◽  
Empirical Mode Decomposition ◽  
Time Domain ◽  
Support Vector ◽  
Hand Gestures ◽  
Mode Decomposition ◽  
Single Sensor ◽  
Mean Variance

Single sensor is employed for classifying four hand gestures from flexor carpum ulnaris. The first three IMFs that are obtained as a result of Empirical Mode Decomposition are taken into consideration. Time domain features like mean, variance, skewness, etc are taken for each IMFs. Support Vector Machine was used for classification task and the extracted model is used for making predictions

A REINTERPRETATION OF EMD BY CUBIC SPLINE INTERPOLATION

2011 ◽  
Vol 03 (04) ◽  
pp. 527-540 ◽  
Author(s):  
MINJEONG PARK ◽  
DONGHOH KIM ◽  
HEE-SEOK OH
Keyword(s):  
Empirical Mode Decomposition ◽  
Time Domain ◽  
Spline Interpolation ◽  
Cubic Spline ◽  
Data Driven ◽  
Constant Frequency ◽  
Cubic Spline Interpolation ◽  
Mode Decomposition ◽  
The Time Domain ◽  
Theoretical Results

Empirical mode decomposition (EMD) is a data-driven technique that decomposes a signal into several zero-mean oscillatory waveforms according to the levels of oscillation. Most of the studies on EMD have focused on its use as an empirical tool. Recently, Rilling and Flandrin, [2008] studied theoretical aspects of EMD with extensive simulations, which allow a better understanding of the method. However, their theoretical results have been obtained by considering constraints on the signal such as equally spaced extrema and constant frequency. The present study investigates the theoretical properties of EMD using cubic spline interpolation under more general conditions on the signal. This study also theoretically supports modified EMD procedures in Kopsinis and Mclaughlin, [2008] and developed for improving the conventional EMD. Furthermore, all analyses are preformed in the time domain where EMD actually operates; therefore, the principle of EMD can be visually and directly captured, which is useful in interpreting EMD as a detection procedure of hidden components.

scholarly journals The Entropy-Based Time Domain Feature Extraction for Online Concept Drift Detection

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1187
Author(s):  
Fengqian Ding ◽  
Chao Luo
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Time Domain ◽  
Concept Drift ◽  
Real Life ◽  
Coarse Grained ◽  
Data Sets ◽  
Mode Decomposition ◽  
Concept Drift Detection

Most of time series deriving from complex systems in real life is non-stationary, where the data distribution would be influenced by various internal/external factors such that the contexts are persistently changing. Therefore, the concept drift detection of time series has practical significance. In this paper, a novel method called online entropy-based time domain feature extraction (ETFE) for concept drift detection is proposed. Firstly, the empirical mode decomposition based on extrema symmetric extension is used to decompose time series, where features in various time scales can be adaptively extracted. Meanwhile, the end point effect caused by traditional empirical mode decomposition can be avoided. Secondly, by using the entropy calculation, the time-domain features are coarse-grained to quantify the structure and complexity of the time series, among which six kinds of entropy are used for discussion. Finally, a statistical process control method based on generalized likelihood ratio is used to monitor the change of the entropy, which can effectively track the mean and amplitude of the time series. Therefore, the early alarm of concept drift can be given. Synthetic data sets and neonatal electroencephalogram (EEG) recordings with seizures annotations data sets are used to validate the effectiveness and accuracy of the proposed method.

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