摘要
Existing eddy current non-destructive testing(NDT) techniques generally do not consider the inclination angle of inclined cracks, which potentially harms a larger region of a tested structure. This work proposes the use of 2 D scan images generated by using pulsed eddy current(PEC) non-destructive testing(NDT) technique in the quantification of the inclination and depth of inclined cracks. The image-based feature extraction technique e ectively identifies the crack axis, which consequently enables extraction of features from the extracted linear scans. The technique extracts linear scans from the images to allow the extraction of three novel image-based features, namely the length of extracted linear scans(LLS), the linear scan skewness(LSS), and the highest value on linear scan(LSmax). The correlation of the three features to surface crack inclination angles and depths were analysed and found to be highly dependent on the crack depths, while only LLS and LSS are correlated to the crack inclination angles.
Existing eddy current non-destructive testing(NDT) techniques generally do not consider the inclination angle of inclined cracks, which potentially harms a larger region of a tested structure. This work proposes the use of 2 D scan images generated by using pulsed eddy current(PEC) non-destructive testing(NDT) technique in the quantification of the inclination and depth of inclined cracks. The image-based feature extraction technique e ectively identifies the crack axis, which consequently enables extraction of features from the extracted linear scans. The technique extracts linear scans from the images to allow the extraction of three novel image-based features, namely the length of extracted linear scans(LLS), the linear scan skewness(LSS), and the highest value on linear scan(LSmax). The correlation of the three features to surface crack inclination angles and depths were analysed and found to be highly dependent on the crack depths, while only LLS and LSS are correlated to the crack inclination angles.
基金
Supported by Malaysia’s Ministry of Higher Education(Grant No.FRGS16-059-0558)