Multifeature Fusion Imaging Based on Machine Learning for Weld Defect Detection Using Induction Scanning Thermography

The detection of weld structures in service is significantly hindered by surface irregularities and blemishes, presenting a challenge to detection technology. To address this issue, the present study proposes a detection platform that integrates magnetic yoke excitation with induction scanning thermography (IST) technology for heating large-scale and complex structures. After the collection and preprocessing of thermography sequence data, a multifeature fusion imaging method is devised based on multiple time-domain thermal feature images input and reevaluates the effectiveness of these individual features in distinguishing defects from backgrounds. Furthermore, an improved phase spatial kurtosis (PSK) feature that utilizes dynamic detection signal mechanisms is proposed. The aforementioned features are combined through a trained artificial neural network (ANN) in order to obtain an estimation. The experimental results demonstrate that the proposed method effectively detects and isolates the defect area in complex backgrounds at the pixel level, enhancing imaging accuracy to 99.6% while significantly suppressing interference from complex background information without generating defect false positives (FPs).