Acoustic emission localization on ship hull structures using a deep learning approach

Abstract

this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor2016-12-23 (andbra);Konferensartikel i tidskriftIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIR