Damage localisation in complex structures, such as those found in aerospace applications, is a difficult problem in the field of structural health monitoring (SHM). The development of an easy to use, fast to apply, cost-effective and very accurate technique is key for the uptake of SHM. Acoustic emission (AE) arising from damage mechanisms and propagating through structure in the form of Lams waves can be detected using piezoelectric sensors mounted on the surface of the structure. The time of arrival (TOA) technique is traditionally used to locate these sources, and relies on the assumption of constant wave speed within the material and uninterrupted wave propagation path between the damage and the sensor. These assumptions are not valid in complex geometry components.

In order to overcome these limitations, Cardiff University developed a new technique (called Delta T Mapping) to locate damage in complex structure with high accuracy by using artificial sources on an area of interest to create training maps. These maps are used to locate subsequent AE events arising from damage events. However, this technique needs high operator expertise to deal with the training maps data (e.g. selecting the correct data) which can be a time consuming process. In this paper, a new and improved fully automatic Delta T Mapping technique is present. Here the correct data in the training maps were identified and selected automatically using a clustering algorithm and a new approach (Minimum Difference approach) is used to determine the damage location. This paper reports experimental validation of the advantages of new The results showed excellent reduction in running time (from 8 hours to only 18 seconds) as well as improved accuracy (location error improved from 3.48mm to 3.13mm in complex geometry).