Acoustic emission localization on ship hull structures using a deep learning approach
George Georgoulas, Vassilios A. Kappatos, George Nikolakopoulos · Vibroengineering PROCEDIA · 2016
In 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 sensor.