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.

Pressure vessels are prone to defects due to environmental conditions, which may cause serious safety hazards to industrial production. The probabilistic ellipse imaging method, based on ultrasonic guided wave, is a common method for locating defects on plate-like structures. In this paper, the research showed that the accuracy of the traditional probabilistic ellipse imaging method was severely affected by the truncation length of the signal. In order to improve the defect location accuracy of the probabilistic elliptic imaging algorithm, an adaptive signal truncation method based on signal difference analysis was proposed, and a novel probabilistic elliptic imaging method was developed. Firstly, the relationship model between the signal difference coefficient (SDC) and the distance coefficient was constructed. Through this model, the distance coefficient of each group signal can be calculated, so that the adaptive truncation length for each group of signals can be determined and the truncated signals used for defect imaging. Secondly, in order to improve the robustness of the new imaging method, the relationship between the defect location accuracy and SDC thresholds were investigated and the optimal threshold was determined. The experimental results showed that the probabilistic ellipse imaging algorithm, based on the new adaptive signal truncation method, can effectively locate a single defect on a pressure vessel.