Automated analysis of acoustic emission signals based on the ISODATA algorithm

Acoustic emission (AE) testing is a widely used technique for the continuous evaluation of damage initiation and propagation in structural components. AE testing can be applied to a wide range of materials, such as metals and fibre-reinforced composites (FRCs). During loading, multiple failure mechanisms can become active, especially in composite materials, resulting in the generation of AE signals with distinctive waveforms and statistical characteristics. By evaluating AE signals based on their characteristics it is possible to group them into clusters and improve the effectiveness of AE in monitoring structural degradation. The clustering process can be effectively carried out using automated clustering algorithms. The applicability of various clustering algorithms for AE data clustering has been considered in several studies. In this paper, the effectiveness of the iterative self-organising data analysis technique (ISODATA) clustering algorithm is evaluated. The AE data considered have been acquired during tensile and flexural tests on glass fibre-reinforced composite samples. The results from automated clustering are compared with manual filtering of the recorded AE signals.