Fast 2D crack profile reconstruction by image processing for Eddy-Current Testing

Abstract

Fast reconstruction of defects geometry is one of the main goal of Eddy Current Non-destructive testing. The information about the type of defects, their shapes, orientations and geometrical characteristics is of great importance in evaluating the impact of defects on structures' integrity and operation safety. From one point of view, the knowledge of the defect shape could give important information about the manufacturing, the use or the cause that have stressed the specimen and generated the defect, and from the other point of view the reconstruction of geometrical parameters of defect could be made easier if the shape of the defect is known. Although many inverse procedures have been developed for Eddy Current Testing, usually high-computational time and resources are requested, hampering the application of 3D reconstruction methods in on-line or in-situ inspections. In this paper a fast 2D defect profile reconstruction technique is presented based on image processing. The main idea of the proposed method is that images obtained from the maps of magnetic field on specimen with flaws, express a trend attributable to a class of known functions. In particular, the shape of the obtained image depends on both the type of defect that the type of measurement probe adopted. Then using a suitable reference image related to the response of the probe as a digital filter to process the acquired image through deconvolution or correlation operations, the shape of the flaw that has generated the image can be retrieved.

Comparison of time and frequency domain features’ immunity against lift-off in pulse-compression eddy current imaging

“…Recently, some of the authors [14,17,35], showed that C-scan images of 𝐴 𝐻 and 𝜗 𝐻 features are characterized by patterns closely resembling Hermite-Gaussian (HG) modes. The HG order depends on the relative orientation of sample surface, coil axis and the measured B component [36,37].…”

“…Precisely, for small defects, i.e. in the limit of validity of the Born approximation for the field scattered by the defect, the defect's image pattern is well approximated by the 2D convolution of the true geometric shape of the defect with a characteristic HG mode acting as Point Spread Function (PSF) [35]. It was also shown that for the present setup, the natural mode is a 𝐻𝐺 (1,1) mode of order (1,1) characterized by a quadrupolar pattern that expands laterally as the frequency decreases.…”

A novel feature extraction method of eddy current testing for defect detection based on machine learning

“…The receiver coil is located between the two exciting coils. The purpose of this design is to reduce or eliminate background signals and increase the sensitivity to anomalies [2,18]. The system provides fast, reliable data transfer by taking advantage of the Ethernet communication.…”

Review on system development in eddy current testing and technique for defect classification and characterization

“…The feedback of output signal will be shown following the applied input signals. [12] An air-core or ferrite-core coil as EC probe [13] Develop circuitry to measurement and functioned a particular type of probe [14] The PWM carrier eddy current for estimation of magnet [15] Multifrequency mode for triple-coil sensor in lift-off detection [16] System by using artificial neural network [17] Simulations for the threedimensional finite element [18] Topological shape optimisation [19] Simplex-mesh refinement is proposed for creating a forward database [20] Lorentz force ECT [21] Pulsed eddy current signal in inspection technique [22] Probe development of the detection system [23] Feeding devices which are used in the eddy current detection of titanium alloy tube [24] An induction motor 's loading being controlled by ECB [25] Lorentz force ECT [21] Image processing with 2D defect profile reconstruction technique [26] Enables the detection of defects lying and deep inside a conducting material with computation 3D with Lorentz force technique in ECT [27] Swarm algorithm is performed wherein the exact forward computations [28] Characterisation of defects on rivets [29] Non-invasive inspection of large conductive structures [30] Constant current AC source for excitation of the probe [31] Type sensor is introduced based on the eddy current principle [32] Filtrate the suspension of multi-walled carbon nanotubes CNTs [33] The braking torque and force analysis in eddy current using axial flux permanent magnet-type [34] Effectively eliminate the distance effects between the surface of the coil [35] Modelling of the measured response in feature extraction for non-linear regressions for [36] Simulated of threedimensional diagnosis of real cracks from two dimensional [37] Imperialist competitive algorithm in solve the problem of natural crack shape reconstruction [38] Characterisation of the crack depth [39] The numerical solution in...…”

“…[17] defect characterisation on ECT using artificial neural networks ANN was predicted deflect overfitting ANN to deal with complex data of the defect properties. Betta.et.al [26] image processing for ECT for 2D crack width of the sensed defect, it is strongly determined along the skimming resolving and dimension of the exciting coil image processing technique in fast reconstruction of defects geometry…”