scholarly journals Review of Pulsed-Eddy-Current Signal Feature-Extraction Methods for Conductive Ferromagnetic Material-Thickness Quantification

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 470 ◽  
Author(s):  
Nalika Ulapane ◽  
Linh Nguyen
Keyword(s):  
Feature Extraction ◽  
Eddy Current ◽  
Eddy Currents ◽  
Ferromagnetic Material ◽  
Extraction Methods ◽  
Computational Time ◽  
Material Thickness ◽  
Pulsed Eddy Current ◽  
Signal Features ◽  
Infrastructure Maintenance

Thickness quantification of conductive ferromagnetic materials has become a common necessity in present-day structural health monitoring and infrastructure maintenance. Recent research has found Pulsed Eddy Current (PEC) sensing, especially the detector-coil-based PEC sensor architecture, to effectively serve as a nondestructive sensing technique for this purpose. As a result, several methods of varying complexity have been proposed in recent years to extract PEC signal features, against which conductive ferromagnetic material thickness behaves as a function, in return enabling thickness quantification owing to functional behaviours. It can be seen that almost all features specifically proposed in the literature for the purpose of conductive ferromagnetic material-thickness quantification are in some way related to the diffusion time constant of eddy currents. This paper examines the relevant feature-extraction methods through a controlled experiment in which the methods are applied to a single set of experimentally captured PEC signals, and provides a review by discussing the quality of the extractable features, and their functional behaviours for thickness quantification, along with computational time taken for feature extraction. Along with this paper, the set of PEC signals and some MATLAB codes for feature extraction are provided as supplementary materials for interested readers.

scholarly journals Image-Based Feature Extraction Technique for Inclined Crack Quantification Using Pulsed Eddy Current

2019 ◽  
Vol 32 (1) ◽  
Author(s):  
Faris Nafiah ◽  
Ali Sophian ◽  
Md Raisuddin Khan ◽  
Syamsul Bahrin Abdul Hamid ◽  
Ilham Mukriz Zainal Abidin
Keyword(s):  
Feature Extraction ◽  
Eddy Current ◽  
Extraction Technique ◽  
Inclined Crack ◽  
Pulsed Eddy Current ◽  
Feature Extraction Technique

scholarly journals Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Amjed S. Al-Fahoum ◽  
Ausilah A. Al-Fraihat
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Eeg Signal ◽  
Property A ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Signal Features ◽  
Frequency Domains ◽  
Auto Regressive ◽  
The Cost

Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.

scholarly journals Intelligent Defect Identification Based on PECT Signals and an Optimized Two-Dimensional Deep Convolutional Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Baoling Liu ◽  
Jun He ◽  
Xiaocui Yuan ◽  
Huiling Hu ◽  
Xuan Zeng ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Eddy Current ◽  
Computational Cost ◽  
Eddy Current Testing ◽  
Two Dimensional ◽  
Convolutional Network ◽  
Defect Identification ◽  
Current Testing ◽  
Pulsed Eddy Current ◽  
Deep Convolutional Network

Accurate and rapid defect identification based on pulsed eddy current testing (PECT) plays an important role in the structural integrity and health monitoring (SIHM) of in-service equipment in the renewable energy system. However, in conventional data-driven defect identification methods, the signal feature extraction is time consuming and requires expert experience. To avoid the difficulty of manual feature extraction and overcome the shortcomings of the classic deep convolutional network (DCNN), such as large memory and high computational cost, an intelligent defect recognition pipeline based on the general Warblet transform (GWT) method and optimized two-dimensional (2-D) DCNN is proposed. The GWT method is used to convert the one-dimensional (1-D) PECT signal to a 2D grayscale image used as the input of 2D DCNN. A compound method is proposed to optimize the baseline VGG16, a well-known DCNN, from four aspects including reducing the input size, adding batch normalization layer (BN) after every convolutional layer(Conv) and fully connection layer (FC), simplifying the FCs, and removing unimportant filters in Convs so as to reduce memory and computational costs while improving accuracy. Through a pulsed eddy current testing (PECT) experiment considering interference factors including liftoff and noise, the following conclusion can be obtained. The time-frequency representation (TFR) obtained by the GWT method not only has excellent ability in terms of the transient component analysis but also is less affected by the reduction of image size; the proposed optimized DCNN can accurately identify defect types without manual feature extraction. And compared to the baseline VGG16, the accuracy obtained by the optimized DCNN is improved by 7%, to about 99.58%, and the memory and computational cost are reduced by 98%. Moreover, compared with other well-known DCNNs, such as GoogLeNet, Inception V3, ResNet50, and AlexNet, the optimized network has significant advantages in terms of accuracy and computational cost, too.

Determining Feature Extraction Parameters for Pulsed Eddy Current Sensor: A Minimisation Problem Approach

2021 ◽  
pp. 1-1
Author(s):  
Faris Nafiah ◽  
Mohammad O. Tokhi ◽  
Gholamhossein Shirkoohi ◽  
Fang Duan ◽  
Zhanfang Zhao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Eddy Current ◽  
Current Sensor ◽  
Eddy Current Sensor ◽  
Pulsed Eddy Current ◽  
Extraction Parameters ◽  
Minimisation Problem

scholarly journals Force-Invariant Improved Feature Extraction Method for Upper-Limb Prostheses of Transradial Amputees

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 843
Author(s):  
Md. Johirul Islam ◽  
Shamim Ahmad ◽  
Fahmida Haque ◽  
Mamun Bin Ibne Reaz ◽  
Mohammad Arif Sobhan Bhuiyan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Spatial Correlation ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Nonlinear Transformation ◽  
Computational Time ◽  
Feature Extraction Method ◽  
Invariant Feature ◽  
Upper Limb Prostheses

A force-invariant feature extraction method derives identical information for all force levels. However, the physiology of muscles makes it hard to extract this unique information. In this context, we propose an improved force-invariant feature extraction method based on nonlinear transformation of the power spectral moments, changes in amplitude, and the signal amplitude along with spatial correlation coefficients between channels. Nonlinear transformation balances the forces and increases the margin among the gestures. Additionally, the correlation coefficient between channels evaluates the amount of spatial correlation; however, it does not evaluate the strength of the electromyogram signal. To evaluate the robustness of the proposed method, we use the electromyogram dataset containing nine transradial amputees. In this study, the performance is evaluated using three classifiers with six existing feature extraction methods. The proposed feature extraction method yields a higher pattern recognition performance, and significant improvements in accuracy, sensitivity, specificity, precision, and F1 score are found. In addition, the proposed method requires comparatively less computational time and memory, which makes it more robust than other well-known feature extraction methods.

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