Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the...

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Bibliographic Details
Published inApplied sciences Vol. 11; no. 7; p. 3186
Main Authors Sahal, Radhya, Alsamhi, Saeed H., Breslin, John G., Brown, Kenneth N., Ali, Muhammad Intizar
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2021
Subjects
Alternative energy sources
Anomalies
Artificial intelligence
auto-detection
Collaboration
cyber-physical
Decision making
Digital twins
Energy industry
Failure
Fault diagnosis
Industry 4.0
Information retrieval
Intelligence
Internet of Things
Knowledge
Manufacturing
operational data
production system
Semantics
Sensors
Service life
Synchronism
Synchronization
Turbines
Wind farms
Wind power
Workflow
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Summary:Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11073186