Digital twin-enabled automated anomaly detection and bottleneck identification in complex manufacturing systems using a multi-agent approach

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dc.contributor.authorLatsou, Christina
dc.contributor.authorFarsi, Maryam
dc.contributor.authorErkoyuncu, John Ahmet
dc.date.accessioned2023-02-21T15:36:40Z
dc.date.available2023-02-21T15:36:40Z
dc.date.issued2023-02-11
dc.description.abstractDigital twin (DT) models are increasingly being used to improve the performance of complex manufacturing systems. In this context, DTs automatically enabling anomaly detection, such as increase in orders, and bottleneck identification, such as shortage of products, can significantly enhance decision-making to mitigate the consequences of the identified bottlenecks. The existing literature has mainly focused on implementing top-down approaches for analysing the bottlenecks without considering the emergent behaviour of micro-level agents, including inventory levels and human resources, and their impact on the macro-level system’s performance. In order to handle the aforementioned challenges, this paper extends the current literature by proposing a novel DT integrated in a multi-agent cyber physical system (CPS) for detecting anomalies in sensor data, while identifying and removing bottlenecks that emerge during the operation of complex manufacturing systems. An extended 5 C CPS architecture, using multi-agent approach, is implemented to allow DT integration. The agent-based simulation technique enables capturing the probabilistic variability, and aggregate parallelism and dynamism of parallel dynamic interactions within the DT-CPS. A new single agent at the exo-level of the multi-level agent-based modelling structure, called the ‘monitoring agent’, is introduced in this research. The agent detects anomalies and identify bottlenecks through communicating with other agents in different levels automatically. The DT-CPS provides feedback automatically to the physical space to remove and mitigate the identified bottlenecks. The proposed DT based multi-agent CPS has been tested successfully on a real case study in a cryogenic warehouse shop-floor from the cell and gene therapy industry. The performance of the studied cryogenic warehouse is continuously measured using real-time sensor data. The analyses of the results show that the proposed DT-CPS improves the utilisation rates of human resources, on average, by 30% supporting decision making and control in complex manufacturing systems.en_UK
dc.description.sponsorshipInnovate UK: 104515. Engineering and Physical Sciences Research Council (EPSRC): EP/R032718/1en_UK
dc.identifier.citationLatsou C, Farsi M, Erkoyuncu JA. (2023) Digital twin-enabled automated anomaly detection and bottleneck identification in complex manufacturing systems using a multi-agent approach, Journal of Manufacturing Systems, Volume 67, April 2023, pp. 242-264en_UK
dc.identifier.issn0278-6125
dc.identifier.urihttps://doi.org/10.1016/j.jmsy.2023.02.008
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19223
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectDigital twinen_UK
dc.subjectCyber physical systemen_UK
dc.subjectAnomaly detectionen_UK
dc.subjectagent-based modellingen_UK
dc.subjectComplex manufacturingen_UK
dc.titleDigital twin-enabled automated anomaly detection and bottleneck identification in complex manufacturing systems using a multi-agent approachen_UK
dc.typeArticleen_UK

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