Acoustic emission source localization in complex pipe structure using multi-task deep learning models

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Tonghao Zhang, Chenxi Xu, Didem Ozevin
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引用次数: 0

Abstract

Localizing defects in long-range pipelines is essential to reduce the inspection time and develop timely repair strategies. The acoustic emission (AE) method is employed to pinpoint the position of defects in pipelines. The conventional 1-D localization algorithm requires time of arrival differences between two sensors, which may not be accurately captured due to the dispersive nature of the pipe structures. The geometric variations such as elbows and welds can influence the propagating elastic waves and, consequently, arrival time. In this study, an AE source localization approach using a deep learning model is developed to tackle the influences of sensor-source distance and geometric variables. The multi-task learning model first identifies the impact of the elbow and subsequently integrates this information when predicting the source location. The proposed model is evaluated on a complex piping system, which features welded elbows in its connections. Incorporating the elbow effect into the model shows a notable improvement in overall accuracy, rising from 53% (conventional method) to 94% (proposed multi-task learning method).
利用多任务深度学习模型定位复杂管道结构中的声发射源
定位长距离管道中的缺陷对于缩短检测时间和制定及时的维修策略至关重要。声发射 (AE) 方法可用于精确定位管道中的缺陷位置。传统的一维定位算法需要两个传感器之间的到达时间差,而由于管道结构的分散性,可能无法准确捕捉到到达时间差。弯头和焊缝等几何变化会影响弹性波的传播,进而影响到达时间。本研究开发了一种使用深度学习模型的 AE 源定位方法,以解决传感器-源距离和几何变量的影响。多任务学习模型首先识别弯头的影响,然后在预测声源位置时整合这些信息。所提出的模型在一个复杂的管道系统中进行了评估,该系统的连接处采用了焊接弯头。将弯头效应纳入模型后,整体准确率显著提高,从 53%(传统方法)提高到 94%(提议的多任务学习方法)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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