NAVMAT: an AI-powered pathway to knowledge sharing on material failures

Abstract

Abstract The paper describes the product development methodology, the architecture, the modules and features of the operating prototype, as well as future challenges of NAVMAT, a naval materials failure management system. The fundamental compound of the knowledge management platform is the recording and classification of a failure incident. NAVMAT supports different types of users, from the first Reporter to the Analyst and Forensic Engineer through appropriate workflows. In these workflows the incident-related information can be provided as text, files, images and videos, which can be easily associated to the incident and provide structured information. NAVMAT provides a number of Artificial Intelligence (AI)-enabled helpers. During the incident recording, NAVMAT brings into play reactive real-time search which suggests related incidents and literature to facilitate the editor. It also speeds up the classification of incidents by providing AI-suggested labels, chosen from the (multi-lingual) concepts contained in the system Ontology. On the other hand, the intelligent indexing and search infrastructure of the system supports easy identification and retrieval of past incidents, reports and publications, by applying Natural Language Processing. The prototype of the described system has been embedded as a web application validated by potential Users and is being prepared for Operation in a fleet environment.