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Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects.

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Title: Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects.
Authors: Falekas, Georgios1 (AUTHOR) gfalekas@ee.duth.gr, Karlis, Athanasios1 (AUTHOR) akarlis@ee.duth.gr
Source: Energies (19961073). Sep2021, Vol. 14 Issue 18, p5933-5933. 1p.
Subject Terms: *Artificial intelligence, *Industry 4.0, *Machinery, *Predictive control systems
Abstract: State-of-the-art Predictive Maintenance (PM) of Electrical Machines (EMs) focuses on employing Artificial Intelligence (AI) methods with well-established measurement and processing techniques while exploring new combinations, to further establish itself a profitable venture in industry. The latest trend in industrial manufacturing and monitoring is the Digital Twin (DT) which is just now being defined and explored, showing promising results in facilitating the realization of the Industry 4.0 concept. While PM efforts closely resemble suggested DT methodologies and would greatly benefit from improved data handling and availability, a lack of combination regarding the two concepts is detected in literature. In addition, the next-generation-Digital-Twin (nexDT) definition is yet ambiguous. Existing DT reviews discuss broader definitions and include citations often irrelevant to PM. This work aims to redefine the nexDT concept by reviewing latest descriptions in broader literature while establishing a specialized denotation for EM manufacturing, PM, and control, encapsulating most of the relevant work in the process, and providing a new definition specifically catered to PM, serving as a foundation for future endeavors. A brief review of both DT research and PM state-of-the-art spanning the last five years is presented, followed by the conjunction of core concepts into a definitive description. Finally, surmised benefits and future work prospects are reported, especially focused on enabling PM state-of-the-art in AI techniques. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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DbLabel: Energy & Power Source
An: 152657099
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PubType: Academic Journal
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  Data: Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects.
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Sep2021, Vol. 14 Issue 18, p5933-5933. 1p.
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  Data: *<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Industry+4%2E0%22">Industry 4.0</searchLink><br />*<searchLink fieldCode="DE" term="%22Machinery%22">Machinery</searchLink><br />*<searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink>
– Name: Abstract
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  Data: State-of-the-art Predictive Maintenance (PM) of Electrical Machines (EMs) focuses on employing Artificial Intelligence (AI) methods with well-established measurement and processing techniques while exploring new combinations, to further establish itself a profitable venture in industry. The latest trend in industrial manufacturing and monitoring is the Digital Twin (DT) which is just now being defined and explored, showing promising results in facilitating the realization of the Industry 4.0 concept. While PM efforts closely resemble suggested DT methodologies and would greatly benefit from improved data handling and availability, a lack of combination regarding the two concepts is detected in literature. In addition, the next-generation-Digital-Twin (nexDT) definition is yet ambiguous. Existing DT reviews discuss broader definitions and include citations often irrelevant to PM. This work aims to redefine the nexDT concept by reviewing latest descriptions in broader literature while establishing a specialized denotation for EM manufacturing, PM, and control, encapsulating most of the relevant work in the process, and providing a new definition specifically catered to PM, serving as a foundation for future endeavors. A brief review of both DT research and PM state-of-the-art spanning the last five years is presented, followed by the conjunction of core concepts into a definitive description. Finally, surmised benefits and future work prospects are reported, especially focused on enabling PM state-of-the-art in AI techniques. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=152657099
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        Value: 10.3390/en14185933
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        Text: English
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      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Industry 4.0
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      – SubjectFull: Machinery
        Type: general
      – SubjectFull: Predictive control systems
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              Text: Sep2021
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