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.
Highlights
Research activity in Electrical Machine (EM) Predictive Maintenance (PM) observes renewed interest in recent years as industrial and commercial applications diversify and expand into novel areas, while their role in them becomes more prominent
Our future work pertains to the creation of a ship generator and propulsion system Digital Twin (DT) and PM of an industrial machine in a factory shop floor adhering to industrial IoT and the Industry 4.0 paradigm
In the final section of this work, we address the open challenges pertaining to DTF
Summary
Research activity in Electrical Machine (EM) Predictive Maintenance (PM) observes renewed interest in recent years as industrial and commercial applications diversify and expand into novel areas, while their role in them becomes more prominent. PM operation, ensures that processes run with optimal efficiency, further cutting down on operating costs and needed reserves These efforts are further reinforced by novel cost-effective sensors, Data Acquisition (DAQ) and evaluation techniques, making EM diagnostics a significantly profitable venture in industry. Great reviews, such as [1,2,3,4,5,6], concerning state-of-the-art PM methods and their application have been published in recent years, addressing techniques with their applications and comparison. To the best of their ability, the authors made an effort not to repeat the reviewed work, but rather provide an outline and create a web of relevant citations
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