Revista Sextante

Applications of Artificial Intelligence in Predictive Maintenance of Naval Engineering Systems

Dimitri A. Papadakis

Department of Marine Engineering and Intelligent Systems, Aegean Institute for Naval Technology Research, Thessaloniki, Greece

Keywords: artificial intelligence, predictive maintenance, naval engineering, condition monitoring, prognostics

Abstract

Naval vessels depend on complex engineering systems, including propulsion plants, power generation, auxiliary machinery, and hull and platform equipment, whose failure can compromise safety, readiness, and mission success. Traditional maintenance based on fixed schedules or reaction to breakdowns is costly and often poorly matched to the real condition of equipment. This article examines the applications of artificial intelligence in the predictive maintenance of naval engineering systems. It reviews the evolution of maintenance strategies, the data acquisition and monitoring infrastructure that underpins predictive approaches, and the machine learning methods used for fault detection, diagnosis, and prognosis. It then considers applications to propulsion, power, and auxiliary systems, the role of digital twins and integrated health management, and the implementation challenges of data quality, model trust, and cybersecurity. Drawing on research in prognostics and health management, machine learning, and naval engineering, the analysis finds that artificial intelligence can reduce unplanned downtime, improve the targeting of maintenance effort, and strengthen operational readiness, but that benefits depend on reliable data, validated models, skilled personnel, and integration with logistics. The article concludes with recommendations for naval organisations seeking to adopt predictive maintenance responsibly and effectively.