Acoustic emission source location in complex structures using full automatic delta T mapping technique

被引:115
作者
Al-Jumaili, Safaa Kh. [1 ,2 ]
Pearson, Matthew R. [1 ]
Holford, Karen M. [1 ]
Eaton, Mark J. [1 ]
Pullin, Rhys [1 ]
机构
[1] Cardiff Univ, Cardiff Sch Engn, Queens Bldg, Cardiff CF24 3AA, S Glam, Wales
[2] Univ Basrah, Basrah, Iraq
关键词
Acoustic emission; Source location; Complex structure; Unsupervised clustering; Delta T mapping technique; SOURCE LOCALIZATION; THIN PLATES; PICKING; SIGNALS; CONCRETE; SENSORS;
D O I
10.1016/j.ymssp.2015.11.026
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
An easy to use, fast to apply, cost-effective, and very accurate non-destructive testing (NDT) technique for damage localisation in complex structures is key for the uptake of structural health monitoring systems (SHM). Acoustic emission (AE) is a viable technique that can be used for SHM and one of the most attractive features is the ability to locate AE sources. The time of arrival (TOA) technique is traditionally used to locate AE sources, and relies on the assumption of constant wave speed within the material and uninterrupted propagation path between the source and the sensor. In complex structural geometries and complex materials such as composites, this assumption is no longer valid. Delta T mapping was developed in Cardiff in order to overcome these limitations; this technique uses artificial sources on an area of interest to create training maps. These are used to locate subsequent AE sources. However operator expertise is required to select the best data from the training maps and to choose the correct parameter to locate the sources, which can be a time consuming process. This paper presents a new and improved fully automatic delta T mapping technique where a clustering algorithm is used to automatically identify and select the highly correlated events at each grid point whilst the "Minimum Difference" approach is used to determine the source location. This removes the requirement for operator expertise, saving time and preventing human errors. A thorough assessment is conducted to evaluate the performance and the robustness of the new technique. In the initial test, the results showed excellent reduction in running time as well as improved accuracy of locating AE sources, as a result of the automatic selection of the training data. Furthermore, because the process is performed automatically, this is now a very simple and reliable technique due to the prevention of the potential source of error related to manual manipulation. (C) 2015 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:513 / 524
页数:12
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