Detecting failure of a material handling system through a cognitive twin. Issue 10 (2022)

• Record Type: Journal Article
• Authors: D'Amico, R.D.; Sarkar, A.; Karray, H.; Addepalli, S.; Erkoyuncu, J.A.
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 10(2022)
• Page Start: 2725
• Page End: 2730
• Publication Date: 2022
• Subjects: Digital twin -- cognitive twin -- ontology -- BFO -- IOF -- CCO -- knowledge graph -- SPARQL -- material handling systems -- Festo MPS; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.10.128
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper describes a methodology for developing a digital twin (DT) based on a rich semantic model and principles of system engineering. The aim is to provide a general model of digital twins (DT) that can improve decision making based on semantic reasoning on real-time system monitoring. The methodology has been tested on a laboratory pilot plant that acts as a material handling system. The key contribution of this research is to propose a generic information model for DT using foundational ontology and principles of systems engineering. The efficacy of the proposed methodology is demonstrated by the automatic detection of a component level failure using semantic reasoning.