A novel baseline-free defect detection and localization method of welded steel plate based on reciprocity loss. (3rd December 2020)

• Record Type: Journal Article
• Authors: He, Tianhao; Xing, Gailan; Li, Yong; Li, Qinfei; Zhou, Shaoping
• Is Part Of: Measurement science & technology. Volume 32:Number 2(2021)
• Publication Date: 2020-12-03
• Subjects: lamb wave -- weld plate-like structures -- reciprocity loss -- baseline-free -- clustering algorithms; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/abb5db
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: In recent years, ultrasonic Lamb waves have been widely applied to the field of non-destructive testing of plate-like structures as they have outstanding advantages, such as low attenuation, high sensitivity, and wide detection range. Current studies about defect-detection of plate-like structures using Lamb waves mostly focus on non-weld plate-like structures, and defect-detection methods are based on baseline data. This paper proposes a novel baseline-free damage inspection method of welded plate-like structures, which is based on the principle of reciprocity loss and combines the OPTICS and K-means intelligent clustering algorithms to achieve accurate defect localization. In order to verify the location accuracy of the clustering defect localization algorithm, this paper performs comparative experiments between the ellipse imaging algorithm and the clustering algorithm, which use baseline data as health signals. The comparative experimental results show that the single-defect location accuracy of the clustering algorithm is greatly improved compared with the traditional ellipse algorithm. Moreover, in order to verify the validity and feasibility of the baseline-free method, this paper applies this method to obtain characteristic signals and combines the clustering algorithm to locate both single-defect and double-defects. The experimental result of baseline-free method shows that this method can successfully detect and locate multiple defects, which gets rid of the dependence of baseline data.