SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets

Abstract

Predictive maintenance has lately proved to be a useful tool for optimizing costs, performance and systems availability. Furthermore, the greater and more complex the system, the higher the benefit but also the less applied: Architectural, computational and complexity limitations have historically ballasted the adoption of predictive maintenance on the biggest systems. This has been especially true in military systems where the security and criticality of the operations do not accept uncertainty. This paper describes the work conducted in addressing these challenges, aiming to evaluate its applicability in a real scenario: It presents a specific design and development for an actual big and diverse ecosystem of equipment, proposing an semi-unsupervised predictive maintenance system. In addition, it depicts the solution deployment, test and technological adoption of real-world military operative environments and validates the applicability.

Keywords

• predictive maintenance
• behavioural anomaly detection
• machine learning
• deep learning
• warships