As electrofusion (EF) technology is widely used in connecting polyethylene (PE) pipes and other plastic pipes or composite pipes, research in safety assessment of EF joints has been of major concern. EF joints with defects are very common in practical applications. These defects may greatly reduce the mechanical performance of the EF joints and threat safety running of the pipeline system. To evaluate hazard of these defects and provide a basic understanding for the failure mechanism of EF joints, a comprehensive study on defects and failure modes is conducted in this work. The defects in EF joints are classified into four categories: poor fusion interface, over welding, voids, and structural deformity. The forming reasons of these defects are analyzed in detail. The mechanical properties of EF joint containing these defects are investigated by conducting peeling tests and sustained hydraulic pressure tests. Test results show that there are three main failure mode of EF joint under inner pressure, that is, cracking through the fusion interface, cracking through the fitting, and cracking through copper wire interface.
Phased Array Ultrasonic Testing (PA-UT) has been proved to be the most feasible way to inspect defects in electrofusion (EF) joints of polyethylene pipes. The recognition of defects in PA-UT images relies on the experience of operators, resulting in inconsistent defective detection rate and low recognition speed. In this paper, an automatic defect recognition model based on the convolutional neural network for PA-UT images was proposed, realizing recognition of four typical defects in EF joint. The proposed recognition model was composed of an anomaly detection model and a defect detection model. The anomaly detection model recognized anomalies of PA-UT images, meeting the requirement of real-time recognition for practical inspection. The defect detection model classified and located defects of abnormal PA-UT images, achieving high accuracy of defects recognition. By comparing detection models, optimizing parameters and augmenting dataset, the anomaly detection model and the defect detection reached a good combination of accuracy and speed. Experiments showed that the proposed recognition model improved recognition speed with high accuracy compared to a single detection model.
With the application of High-density polyethylene (HDPE) pipe with thick wall in nuclear power plant (NPP), great attention has been paid to the safety of the pipeline’s joints, which can be assessed by phased array ultrasonic testing (PAUT). PAUT creates constructive interference of acoustic waves to generate focused beams according to delay law based on time-of-flight. However, due to the existence of acoustic attenuation and dispersion, waveform distortion occurs when ultrasonic pulse propagates in HDPE, which will accumulate with the increase of propagation distance, and then results in imaging errors. In this paper, the relationship of acoustic attenuation and dispersion in HDPE was obtained by numerical simulation in Field II®, which can be verified by the experiment of our previous work. Besides, the investigation of the waveform distortion revealed the linear relation between peak offset and propagation distance. Considering the relation, an improved delay law was proposed to increase the intensity of ultrasonic field. This improved delay law was compared with the conventional one by numerical simulation of ultrasonic field and PAUT experiments, which showed that the improved delay law could increase the image sensitivity.
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