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Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8874643/ https://www.ncbi.nlm.nih.gov/pubmed/35214332 http://dx.doi.org/10.3390/s22041430 |
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author | Ademujimi, Toyosi Prabhu, Vittaldas |
author_facet | Ademujimi, Toyosi Prabhu, Vittaldas |
author_sort | Ademujimi, Toyosi |
collection | PubMed |
description | 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 Networks (BNs) for fault diagnostics at equipment and factory levels. Specifically, the co-simulation model is engineered by using cyber–physical system (CPS) consisting of networked sensors, high-fidelity simulation model of each equipment, and a detailed discrete-event simulation (DES) model of the factory. The proposed DT approach enables injection of faults in the virtual system, thereby alleviating the need for expensive factory-floor experimentation. It should be emphasized that this approach of injecting faults eliminates the need for obtaining balanced data that include faulty and normal factory operations. We propose a Structural Intervention Algorithm (SIA) in this paper to first detect all possible directed edges and then distinguish between a parent and an ancestor node of the BN. We engineered a DT research test-bed in our laboratory consisting of four industrial robots configured into an assembly cell where each robot has an industrial Internet-of-Things sensor that can monitor vibrations in two-axes. A detailed equipment-level simulator of these robots was integrated with a detailed DES model of the robotic assembly cell. The resulting DT was used to carry out interventions to learn a BN model structure for fault diagnostics. Laboratory experiments validated the efficacy of the proposed approach by accurately learning the BN structure, and in the experiments, the accuracy obtained by the proposed approach (measured using Structural Hamming Distance) was found to be significantly better than traditional methods. Furthermore, the BN structure learned was found to be robust to variations in parameters, such as mean time to failure (MTTF). |
format | Online Article Text |
id | pubmed-8874643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88746432022-02-26 Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems Ademujimi, Toyosi Prabhu, Vittaldas Sensors (Basel) Article 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 Networks (BNs) for fault diagnostics at equipment and factory levels. Specifically, the co-simulation model is engineered by using cyber–physical system (CPS) consisting of networked sensors, high-fidelity simulation model of each equipment, and a detailed discrete-event simulation (DES) model of the factory. The proposed DT approach enables injection of faults in the virtual system, thereby alleviating the need for expensive factory-floor experimentation. It should be emphasized that this approach of injecting faults eliminates the need for obtaining balanced data that include faulty and normal factory operations. We propose a Structural Intervention Algorithm (SIA) in this paper to first detect all possible directed edges and then distinguish between a parent and an ancestor node of the BN. We engineered a DT research test-bed in our laboratory consisting of four industrial robots configured into an assembly cell where each robot has an industrial Internet-of-Things sensor that can monitor vibrations in two-axes. A detailed equipment-level simulator of these robots was integrated with a detailed DES model of the robotic assembly cell. The resulting DT was used to carry out interventions to learn a BN model structure for fault diagnostics. Laboratory experiments validated the efficacy of the proposed approach by accurately learning the BN structure, and in the experiments, the accuracy obtained by the proposed approach (measured using Structural Hamming Distance) was found to be significantly better than traditional methods. Furthermore, the BN structure learned was found to be robust to variations in parameters, such as mean time to failure (MTTF). MDPI 2022-02-13 /pmc/articles/PMC8874643/ /pubmed/35214332 http://dx.doi.org/10.3390/s22041430 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ademujimi, Toyosi Prabhu, Vittaldas Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems |
title | Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems |
title_full | Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems |
title_fullStr | Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems |
title_full_unstemmed | Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems |
title_short | Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems |
title_sort | digital twin for training bayesian networks for fault diagnostics of manufacturing systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8874643/ https://www.ncbi.nlm.nih.gov/pubmed/35214332 http://dx.doi.org/10.3390/s22041430 |
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