Automated classification of eddy current signatures during manual inspection

Šmı́d, Radislav; Dočekal, A.; Kreidl, M.

doi:10.1016/j.ndteint.2004.12.004

Cited by 29 publications

(18 citation statements)

References 9 publications

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“…The U-BRAIN algorithm has the further advantage to showing explicitly the rule underlying the process. Compared to other works on the same data [69] the CBIR-ANN and CBIR-U-BRAIN chains have shown better results.…”

Section: Setmentioning

confidence: 64%

Feature extraction and soft computing methods for aerospace structure defect classification

D’Angelo¹,

Rampone²

2016

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This study concerns the effectiveness of several techniques and methods of signals processing and data interpretation for the diagnosis of aerospace structure defects. This is done by applying different known feature extraction methods, in addition to a new CBIR-based one;and some soft computing techniques including a recent HPC parallel implementation of the U-BRAIN learning algorithm on Non Destructive Testing data. The performance of the resulting detection systems are measured in terms of Accuracy, Sensitivity, Specificity, and Precision. Their effectiveness is evaluated by the Matthews correlation, the Area Under Curve (AUC), and the F-Measure. Several experiments are performed on a standard dataset of eddy current signal samples for aircraft structures. Our experimental results evidence that the key to a successful defect classifier is the feature extraction method -namely the novel CBIR-based one outperforms all the competitorsand they illustrate the greater effectiveness of the U-BRAIN algorithm and the MLP neural network among the soft computing methods in this kind of application.

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Section: Setmentioning

confidence: 64%

Feature extraction and soft computing methods for aerospace structure defect classification

D’Angelo¹,

Rampone²

2016

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“…Weld inspections are costly in terms of time and money, both during fabrication and in-service. A variety of non-destructive evaluation (NDE) methods are used for weld inspection including radiographic testing (RT) [1,2], eddy current [3,4], magnetic particle (MT) [5], dye penetrant [6], and ultrasonic (conventional, shear wave, or phased array) [7][8][9]. There are challenges associated with each of these NDE methods for weld defect detection.…”

Section: Introductionmentioning

confidence: 99%

Infrared Thermography for Weld Inspection: Feasibility and Application

2018

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Traditional ultrasonic testing (UT) techniques have been widely used to detect surface and sub-surface defects of welds. UT inspection is a contact method which burdens the manufacturer by storing hot specimens for inspection when the material is cool. Additionally, UT is only valid for 5 mm specimens or thicker and requires a highly skilled operator to perform the inspections and interpret the signals. Infrared thermography (IRT) has the potential to be implemented for weld inspections due to its non-contact nature. In this study, the feasibility of using IRT to overcome the limitations of UT inspection is investigated to detect inclusion, porosity, cracking, and lack of fusion in 38 weld specimens with thicknesses of 3, 8 and 13 mm. UT inspection was also performed to locate regions containing defects in the 8 mm and 13 mm specimens. Results showed that regions diagnosed with defects by the UT inspection lost heat faster than the sound weld. The IRT method was applied to six 3 mm specimens to detect their defects and successfully detected lack of fusion in one of them. All specimens were cut at the locations indicated by UT and IRT methods which proved the presence of a defect in 86% of the specimens. Despite the agreement with the UT inspection, the proposed IRT method had limited success in locating the defects in the 8 mm specimens. To fully implement in-line IRT-based weld inspections more investigations are required.

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“…Neural networks and SVM have been compared for defect shape reconstruction in ECT, with SVM giving better results [28]. Automated classification using SVM has been investigated in ECT [29,30] and fault diagnosis [31], however, the SVM has not been combined with PEC for improve defect classification in aircraft structures, which leads to the application of SVM in this work.…”

Section: Introductionmentioning

confidence: 99%