Deep machine learning for structural health monitoring on ship hulls using acoustic emission method

• Published in: Ships and offshore structures Vol. 16; no. 4; pp. 440 - 448
• Main Authors: Karvelis, Petros, Georgoulas, George, Kappatos, Vassilios, Stylios, Chrysostomos
• Format: Journal Article
• Language: English
• Published: Cambridge Taylor & Francis 21.04.2021; Taylor & Francis Ltd
• Subjects: Acoustic emission; Acoustic fatigue; Artificial neural networks; Catastrophic events; Classification; Corrosion; Corrosion fatigue; Crack propagation; Damage detection; deep belief networks; deep learning; Discrete cosine transform; Fatigue cracking; Fatigue failure; Fracture mechanics; Hulls; Machine learning; Piecewise Aggregate Approximation; Ship hull; Ship hulls; source localisation; Structural damage; Structural health monitoring
• ISSN: 1744-5302; 1754-212X
• DOI: 10.1080/17445302.2020.1735844

Corrosion, fatigue and corrosion-fatigue cracking are the most pervasive types of structural problems experienced by ship structures. These damage modes, can potentially lead to unanticipated out of service time or catastrophic failure. Acoustic Emission is gaining ground as a complementary Structural Health Monitoring (SHM) technique, since it can offer real-time damage detection. Deep learning, on the other hand, has shown great success over the last years for a large number of applications. In this paper, the SHM on ship hulls is treated as a classification problem. Firstly, the AE signals are transformed, using the Discrete Cosine Transform, followed by a dimensionality reduction stage. Afterwards, a Deep Neural Network is employed by the classification module. The proposed approach was validated and the results indicate that our proposed method can be very effective and efficient, selecting the optimum AE sensor positions and providing almost perfect localisation results.