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.