Neural localization of acoustic emission sources in ship hulls

Abstract

In this paper a radial-basis-function neural network is used to localize acoustic emission events in ship hulls. It is shown that using a tiny network configuration and a small set of robust features, selected automatically by the K-means algorithm from a superset of 90 signal parameters, the location of a single event can be classified efficiently into three typical areas found in ship hulls. In simulation experiments, where a stiffened plate model is partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is 100% using four sensors and only four features per sensor. In the proposed method, the feature set is adapted and estimated automatically in cases of noisy environments. Robust acoustic emission localization rates, greater than 90%, are achieved using less than ten features per sensor in case of additive white Gaussian noise at 0 dB SNR or more.