scholarly journals Research on Eddy Current Imaging Detection of Surface Defects of Metal Plates Based on Compressive Sensing

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Weiquan Deng ◽  
Bo Ye ◽  
Guoyong Huang ◽  
Jiande Wu ◽  
Mengbao Fan ◽  
...  

Accurate detection and quantitative evaluation of defects and damage in metal plates is a crucial task in a range of technological applications, such as maintaining the integrity, enhancing the safety, and assuring the reliability of structures. There is scope for improving eddy current testing methods by incorporating compressive sensing (CS) in the inspection process. The key scientific problems in eddy current imaging of defects of metal plates are sparse representations and transform domain mapping, sparse testing constraints, and sparse image reconstruction. The main research content of this paper is as follows. We first provide basic theory based on research of sparse representations, transform domain mapping, sparse matrices, sparse transform matrices, and signal recovery a priori errors. We then propose information-recovery methods for completing compressive sensing. Third, we establish an experimental system for validating theories and methods. Finally, we establish theories and methods for eddy current imaging of metal plates.

2019 ◽  
Vol 19 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Linus Michaeli ◽  
Ján Šaliga ◽  
Pavol Dolinský ◽  
Imrich Andráš

Abstract Compressive sensing is a processing approach aiming to reduce the data stream from the observed object with the inherent sparsity using the optimal signal models. The compression of the sparse input signal in time or in the transform domain is performed in the transmitter by the Analog to Information Converter (AIC). The recovery of the compressed signal using optimization based on the differential evolution algorithm is presented in the article as an alternative to the faster pseudoinverse algorithm. Pseudoinverse algorithm results in an unambiguous solution associated with lower compression efficiency. The selection of the mathematically appropriate signal model affects significantly the compression efficiency. On the other hand, the signal model influences the complexity of the algorithm in the receiving block. The suitability of both recovery methods is studied on examples of the signal compression from the passive infrared (PIR) motion sensors or the ECG bioelectric signals.


2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.


1992 ◽  
Author(s):  
Rodrigo de Oliveira Bohbot ◽  
Dominique Lesselier ◽  
Bernard Duchene ◽  
Nathalie Coutanceau

Author(s):  
G. L. Fitzpatrick ◽  
D. K. Thome ◽  
R. L. Skaugset ◽  
E. Y. C. Shih ◽  
William C. L. Shih

2015 ◽  
Vol 237 ◽  
pp. 136-141
Author(s):  
Wojciech Jóźwik ◽  
Tomasz Samborski

The article presents the results of the influence of geometrical features of defects in materials on the level of identification by the eddy current method. The study involved the inner ring of the tapered roller bearing. Four test defects, located at a constant distance from the inner surface, and a subsurface marker defect were performed in the treadmill of the tested ring. The test defects had a constant cross-sectional area in a perpendicular direction to the surface of the eddy current head. The geometrical features of each defect were the following: shape, the perimeter of the defect projected onto the surface of the ring, and the width and height of the defect projected on the face of the measuring head. The study involved an inner surface (subsurface defect detection) and external surface (the study of surface defects). It has been shown that the shape of the defect affects the level of detection using the eddy current method.


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