Optimum Position of Acoustic Emission Sensors for Ship Hull Structural Health Monitoring Based on Deep Machine Learning

GEORGE, GEORGOULAS and PETROS, KARVELIS and VASILIS, TZITZILONIS and VASSILIOS, KAPPATOS (2017) Optimum Position of Acoustic Emission Sensors for Ship Hull Structural Health Monitoring Based on Deep Machine Learning. In: Sixth International Conference on Advances in Mechanical and Robotics Engineering - AMRE 2017, 09-10 December, 2017, Rome, Italy.

[img]
Preview
Text
20180215_112142.pdf - Published Version

Download (832kB) | Preview
Official URL: https://www.seekdl.org/conferences/paper/details/9...

Abstract

In this paper a method for the estimation of the optimum sensor positions for acoustic emission localization on ship hull structures is presented. The optimum sensor positions are treated as a classification (localization) problem based on a deep learning paradigm. In order to avoid complex and timeconsuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. The optimum sensor position is defined by the maximum localization rate. 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 99.5 %, using only a single sensor.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Acoustic emission, optimum sensor positions, ship hull, deep machine learning
Depositing User: Mr. John Steve
Date Deposited: 10 Mar 2019 09:23
Last Modified: 10 Mar 2019 09:23
URI: http://publications.theired.org/id/eprint/265

Actions (login required)

View Item View Item