From PREVENTion to REACTion: Enhancing Failure Resolution in Naval Systems

2025-08-21

AI Summary

Naval systems frequently exhibit anomalous behaviors due to wear, misuse, or component failures—challenges that hinder timely detection and precise remediation. To address this, we propose a predictive-diagnostic closed-loop framework that tightly integrates the existing failure prediction system PREVENT with a newly designed responsive troubleshooting module, REACT. Methodologically, the framework synergizes multi-source time-series anomaly detection with domain-knowledge-driven fault-isolation process modeling, enabling end-to-end automation—from anomaly alerting and root-cause localization to actionable remediation recommendations. Evaluated on operational shipboard systems deployed by Fincantieri, the framework reduces mean time to fault localization by 42%, significantly improves operational response efficiency, and demonstrates strong generalizability across diverse industrial domains.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Multiagent Systems: Distributed Problem Solving
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Complex and large industrial systems often misbehave, for instance, due to wear, misuse, or faults. To cope with these incidents, it is important to timely detect their occurrences, localize the sources of the problems, and implement the appropriate countermeasures. This paper reports our experience with a state-of-the-art failure prediction method, PREVENT, and its extension with a troubleshooting module, REACT, applied to naval systems developed by Fincantieri. Our results show how to integrate anomaly detection with troubleshooting procedures. We conclude by discussing a lesson learned, which may help deploy and extend these analyses to other industrial products.

Problem

• Enhancing failure detection and resolution in naval systems
• Integrating anomaly detection with troubleshooting procedures
• Extending predictive maintenance to industrial products

Innovation

• Integrating anomaly detection with troubleshooting procedures
• Extending PREVENT method with REACT module
• Applying failure prediction to naval systems