Fast Eddy Current Testing Defect Classification Using Lissajous Figures

D’Angelo, Gianni; Laracca, Marco; Rampone, Salvatore; Betta, G.

doi:10.1109/tim.2018.2792848

Cited by 46 publications

(18 citation statements)

References 26 publications

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“…For example, and can straightforwardly be obtained from the signal [32] while and can be obtained using a camera and image processing techniques [59,60]. D'Angelo, et al [61] proposed a method for automatic classification of isolated defects from 2D scans using Lissajous figures. While this method can be only applied on isolated cracks, it has potential to be further extended to allow extraction of this method's a priori information about crack clusters from 2D scans.…”

Section: Comparison With Single Crack Sizing Methodsmentioning

confidence: 99%

“…To achieve this, extensive modelling work, based on defect parameters observed in railway clustered RCF cracks, has been undertaken to determine the influence of important crack parameters on the ACFM signal, and to be used as the training and validation databases for the ANN. D'Angelo, et al [61] proposed a method for automatic classification of isolated defects from 2D scans using Lissajous figures. While this method can be only applied on isolated cracks, it has potential to be further extended to allow extraction of this method's a priori information about crack clusters from 2D scans.…”

Section: Introductionmentioning

confidence: 99%

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Characterisation of clustered cracks using an ACFM sensor and application of an artificial neural network

Rowshandel

Nicholson

Shen

et al. 2018

NDT & E International

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The alternating current field measurement (ACFM) technique can be applied for surface-breaking fatigue crack detection and sizing; the link between the ACFM signal and crack size is well understood for individual cracks. However, the ACFM response to multiple clustered cracks is significantly different to that of isolated cracks. In railway rails the high wheel-rail forces can lead to rolling contact fatigue (RCF) cracks. Often cracks appear together in small clusters or in long stretches. The accurate characterisation of such fatigue cracks is essential for carrying out efficient and safe repair and maintenance. This paper presents a method for sizing the important sub-surface section of multiple cracks using ACFM via the application of an artificial neural network (ANN). The approach is demonstrated using a railway case study: a simulation-based dataset of signal response covering the range of RCF cracks typically seen in in-service railway tracks has been generated to give a thorough representation of the effect of clustered crack parameters on the ACFM response. A 5×5×2×1 multi-layer ANN has been optimised and trained using the validated simulation database to learn the inverse relationship between the crack pocket length (desired output) and the ACFM signal for a given cluster of RCF cracks. The network has been evaluated on a set of experimental data to size cracks of known dimensions from ACFM measurements and also on unseen simulation data. Results from both simulation and experiment show that the approach presented can be used to size clustered cracks to approximately the same degree of accuracy as is possible for isolated cracks.

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Section: Comparison With Single Crack Sizing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterisation of clustered cracks using an ACFM sensor and application of an artificial neural network

Rowshandel

Nicholson

Shen

et al. 2018

NDT & E International

View full text Add to dashboard Cite

show abstract

“…Suppose H is a grayscale image and G stands for the image after thresholding. The i − th region is binarized using Formula (5). An example is shown in Figure 8d.…”

Section: Image Preprocessingmentioning

confidence: 99%

“…In recent years, many excellent technical solutions for defect detection have been proposed by researchers. The ultrasonic testing technique [2,3], eddy current testing technique [4][5][6] and vision inspection technique [7][8][9][10] are widely used. Among them, in practical production, the vision inspection technique has been successfully adopted for surface detection of industrial products because of its convenience, effectiveness and cost performance.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Ring Multi-Defect Stereo Detection System Based on Multi-Camera Vision Technology

Zhang

Gong

2020

Sensors

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Magnetic rings are the most widely used magnetic material product in industry. The existing manual defect detection method for magnetic rings has high cost, low efficiency and low precision. To address this issue, a magnetic ring multi-defect stereo detection system based on multi-camera vision technology is developed to complete the automatic inspection of magnetic rings. The system can detect surface defects and measure ring height simultaneously. Two image processing algorithms are proposed, namely, the image edge removal algorithm (IERA) and magnetic ring location algorithm (MRLA), separately. On the basis of these two algorithms, connected domain filtering methods for crack, fiber and large-area defects are established to complete defect inspection. This system achieves a recognition rate of 100% for defects such as crack, adhesion, hanger adhesion and pitting. Furthermore, the recognition rate for fiber and foreign matter defects attains 92.5% and 91.5%, respectively. The detection speed exceeds 120 magnetic rings per minutes, and the precision is within 0.05 mm. Both precision and speed meet the requirements of real-time quality inspection in actual production.

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“…Over the last two decades, various soft computing techniques such as Artificial Neural Networks (ANNs), Decision Trees, Fuzzy Logic (FL), Adaptive Neuro Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM) have been increasingly implemented to tackle civil engineering problems [12][13][14][15][16][17][18]. Some of these techniques have also been utilized for the prediction of the bond strength and shear capacity of the FRP bars in concrete.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach

et al. 2019

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Accurate prediction of bond behavior of fiber reinforcement polymer (FRP) concrete has a pivotal role in the construction industry. This paper presents a soft computing method called multi-gene genetic programming (MGGP) to develop an intelligent prediction model for the bond strength of FRP bars in concrete. The main advantage of the MGGP method over other similar methods is that it can formulate the bond strength by combining the capabilities of both standard genetic programming and classical regression. A number of parameters affecting the bond strength of FRP bars were identified and fed into the MGGP algorithm. The algorithm was trained using an experimental database including 223 test results collected from the literature. The proposed MGGP model accurately predicts the bond strength of FRP bars in concrete. The newly defined predictor variables were found to be efficient in characterizing the bond strength. The derived equation has better performance than the widely-used American Concrete Institute (ACI) model.

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Fast Eddy Current Testing Defect Classification Using Lissajous Figures

Cited by 46 publications

References 26 publications

Characterisation of clustered cracks using an ACFM sensor and application of an artificial neural network

Characterisation of clustered cracks using an ACFM sensor and application of an artificial neural network

Magnetic Ring Multi-Defect Stereo Detection System Based on Multi-Camera Vision Technology

An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach

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