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

ABSTRACT

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.

KEYWORDS:

• Ship hull
• structural health monitoring
• deep learning
• deep belief networks
• acoustic emission
• source localisation
• Piecewise Aggregate Approximation

Disclosure statement

No potential conflict of interest was reported by the author(s).

ORCID

Petros Karvelis http://orcid.org/0000-0002-0483-4868

George Georgoulas http://orcid.org/0000-0001-9701-4203

Vassilios Kappatos http://orcid.org/0000-0002-5314-2622

Chrysostomos Stylios http://orcid.org/0000-0002-2888-6515