Automatic Classification of Acoustic Emission Patterns

: The problem of automatic classification of acoustic emission signals using techniques derived from pattern recognition is addressed in this paper. The data were taken from laboratory experimental work on a box girder of a bridge in which the acoustic emission (AE) generation mechanism and location were monitored. Two statistical methods and a neural network procedure have been used to classify the data into groups representing different AE generation mechanisms. The classifiers are constructed using the traditional AE features – four parameters from each burst. Principal component analysis is used to reduce the dimension of the AE data feature vectors to two dimensions, resulting in simple visualisations of the data.