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Idea of AE separation from unpredicted source area during AE testing by autoencoder
1  Tokyo Institute of Technology


When conducting AE testing, there is an industrial need to separate AE from monitoring area to that from outside of the area in some cases. In this study, usefulness of autoencoder to solve this problem is discussed by simple experiment using an isotropic thin steel ruler. It was shown that a single trained autoencoder can be used for separating AE signals with variety of waveforms from monitoring area to those from outside of monitoring area when setting an appropriate threshold.

Keywords: Acoustic Emission; Artificial Neural Network (ANN); Autoencoder; Anomaly data separation