Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 4; p. 1430
• Main Authors: Ademujimi, Toyosi, Prabhu, Vittaldas
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.02.2022; MDPI
• Subjects: Algorithms; Bayesian network; Computational linguistics; Design of experiments; digital twin; Fault diagnosis; fault diagnostics; Heuristic; Industrial Internet of Things; Innovations; Language processing; Learning; Manufacturing; Natural language interfaces; OEM; Ontology; Random variables; Robots; Sensors; Simulation; small data set; smart manufacturing; structure learning; Subject specialists; Twins; New York; fault diagnostics; digital twin; smart manufacturing; Bayesian network; small data set; structure learning

Smart manufacturing systems are being advocated to leverage technological advances that enable them to be more resilient to faults through rapid diagnosis for performance assurance. In this paper, we propose a co-simulation approach for engineering digital twins (DTs) that are used to train Bayesian...