Crack Shape Reconstruction in Eddy Current Testing Using Machine Learning Systems for Regression

Bernieri, Andrea; Ferrigno, Luigi; Laracca, Marco; Molinara, Mario

doi:10.1109/tim.2008.919011

Cited by 115 publications

(51 citation statements)

References 17 publications

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“…The authors are investigating the field of Non-Destructive ECT for many years, proposing probes, measurement methods and excitation strategies to optimize the defect characterization by ECT methods [6,7,9,10,12,[17][18][19]. Currently their research activity focuses on the use of signals in the frequency domain with the aim to improve the sensitivity of defect detection in some particular application fields, and especially for small and sub-superficial cracks.…”

Section: Introductionmentioning

confidence: 99%

An experimental comparison of multi-frequency and chirp excitations for eddy current testing on thin defects

et al. 2015

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Section: Introductionmentioning

confidence: 99%

An experimental comparison of multi-frequency and chirp excitations for eddy current testing on thin defects

et al. 2015

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Section: Introductionmentioning

confidence: 99%

“…SVMs have shown promise analyzing eddy current responses [22,23]. A performance comparison between ANN and SVM has shown that SVM regression performed better for solving crack characterization problems in conventional ECT [22]. PEC analysis comparing PCA and independent component analysis (ICA), each processed using support vector machines (SVMs), has also been investigated for defect classification in multi-layer aluminum structures [23].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of machine learning tools for inspection of steam generator tube structures using pulsed eddy current

Buck

Underhill

Morelli

et al. 2017

AIP Conference Proceedings

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Abstract. Degradation of nuclear steam generator (SG) tubes and support structures can result in a loss of reactor efficiency. Regular in-service inspection, by conventional eddy current testing (ECT), permits detection of cracks, measurement of wall loss, and identification of other SG tube degradation modes. However, ECT is challenged by overlapping degradation modes such as might occur for SG tube fretting accompanied by tube off-set within a corroding ferromagnetic support structure. Pulsed eddy current (PEC) is an emerging technology examined here for inspection of Alloy-800 SG tubes and associated carbon steel drilled support structures. Support structure hole size was varied to simulate uniform corrosion, while SG tube was off-set relative to hole axis. PEC measurements were performed using a single driver with an 8 pick-up coil configuration in the presence of flat-bottom rectangular frets as an overlapping degradation mode. A modified principal component analysis (MPCA) was performed on the time-voltage data in order to reduce data dimensionality. The MPCA scores were then used to train a support vector machine (SVM) that simultaneously targeted four independent parameters associated with; support structure hole size, tube off-centering in two dimensions and fret depth. The support vector machine was trained, tested, and validated on experimental data. Results were compared with a previously developed artificial neural network (ANN) trained on the same data. Estimates of tube position showed comparable results between the two machine learning tools. However, the ANN produced better estimates of hole inner diameter and fret depth. The better results from ANN analysis was attributed to challenges associated with the SVM when non-constant variance is present in the data.

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“…The usage of eddy current testing techniques has significantly increased since the 1950s in the aeronautical [3,4] and nuclear [5,6] industries, among others. In recent years, state-of-the-art electronic components and processors have been applied to build eddy current instrumentation [7][8][9]. Moreover, eddy current testing techniques have been employed to analyze materials: Konoplyuk predicted the matrix microstructure in ductile cast irons [10], Mercier et al classified steel decarburizing samples [11], and Wrzuszczak et al detected defects on conducting materials [12].…”

Section: Introductionmentioning

confidence: 99%

RBF-Neural Network Applied to the Quality Classification of Tempered 100Cr6 Steel Cams by the Multi-Frequency Nondestructive Eddy Current Testing

Martínez-Martínez

García-Martín

Gómez-Gil

2017

Metals

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This article proposes a Radial Basis Function Artificial Neural Network (RBF-ANN) to classify tempered steel cams as correctly or incorrectly treated pieces by using multi-frequency nondestructive eddy current testing. Impedances at five frequencies between 10 kHz and 300 kHz were employed to perform the binary sorting. The ANalysis Of VAriance (ANOVA) test was employed to check the significance of the differences between the impedance samples for the two classification groups. Afterwards, eleven classifiers were implemented and compared with one RBF-ANN classifier: ten linear discriminant analysis classifiers and one Euclidean distance classifier. When employing the proposed RBF-ANN, the best performance was achieved with a precision of 95% and an area under the Receiver Operating Characteristic (ROC) curve of 0.98. The obtained results suggest RBF-ANN classifiers processing multi-frequency impedance data could be employed to classify tempered steel DIN 100Cr6 cams with a better performance than other classical classifiers.

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Crack Shape Reconstruction in Eddy Current Testing Using Machine Learning Systems for Regression

Cited by 115 publications

References 17 publications

An experimental comparison of multi-frequency and chirp excitations for eddy current testing on thin defects

An experimental comparison of multi-frequency and chirp excitations for eddy current testing on thin defects

Evaluation of machine learning tools for inspection of steam generator tube structures using pulsed eddy current

RBF-Neural Network Applied to the Quality Classification of Tempered 100Cr6 Steel Cams by the Multi-Frequency Nondestructive Eddy Current Testing

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