Inspection of Defects in Weld Using Differential Array ECT Probe and Deep Learning Algorithm

Abstract

Inspection of defects in weld has both safety and economic significance in industries. However, it is still a challenging problem to be studied due to the interference of the uneven surface of the weld and nonuniformity of the material. This article proposes a new flexible eddy current testing (ECT) probe with differential multimodes for weld inspection. The probe is constructed based on a flexible printed circuit board (PCB). Consequently, it can be bended according to the uneven surface of a weld sample. The differences of pairs of coils are recoded as the outputs of the probe, in which the common mode noises and background signals are canceled. Considering the different orientations of the differential pair coils, the probe has four modes. Different modes complement each other in terms of sensitivity to different kinds of defects. The effect of the excitation frequency has been studied, and 1 MHz is chosen as the operating frequency of the probe. An image preprocessing algorithm called spatial domain filtering and gradient feature edge extraction (SDF-GFEE) is proposed for suppressing the noise of the experimental images. A target detection algorithm based on deep neural network named mask region convolution neutral network (Mask-RCNN) is utilized to identify defects from the images. Experimental results demonstrate that this method is capable of identifying the groove defect with dimensions 3 mm (length) ×0.1 mm (width) ×0.5 mm (depth) and flat bottom hole with dimensions Φ0.8 mm ×0.5 mm in a weld sample.