Cognitive digital twin: An approach to improve the maintenance management. (August 2022)

• Record Type: Journal Article
• Authors: D'Amico, Rosario Davide; Erkoyuncu, John Ahmet; Addepalli, Sri; Penver, Steve
• Is Part Of: CIRP journal of manufacturing science and technology. Volume 38(2022)
• Page Start: 613
• Page End: 630
• Publication Date: 2022-08
• Subjects: Basic Formal Ontology (BFO) -- Cognitive twin -- Digital twin -- Ontology -- Predictive maintenance -- Top-level ontology (TLO); Manufacturing processes -- Periodicals; 670.5
• DOI: 10.1016/j.cirpj.2022.06.004
• Languages: English
• ISSNs: 1755-5817

Abstract

Abstract: Digital twin (DT) technology allows the user to monitor the asset, specifically over the operation and service phase of the life cycle, which is the longest-lasting phase for complex engineering assets. This paper aims to present a thematic review of DTs in terms of the technology used, applications, and limitations specifically in the context of maintenance. This review includes a systematic literature review of 59 articles on semantic digital twins in the maintenance context. Key performance indicators and explanations of the main concepts constituting the DT have been presented. This article contains a description of the evolution of DTs together with their characterisation for maintenance purposes. It provides an ontological approach to develop DT and improve the maintenance management leading to the creation of a structured DT or a Cognitive Twin (CT). Moreover, it points out that using a top-level ontology approach should be the starting point for the creation of CT. Enabling the creation of the digital framework that will break down silos, ensuring a perfect integration in a network of twins' scenario.