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

Falekas, Georgios; Karlis, Athanasios

doi:10.3390/en14185933

Cited by 51 publications

(17 citation statements)

References 62 publications

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“…Data-driven approaches are substantially beneficial for new industrial and research paradigms such as Industry 4.0 and the emergence of Big Data. New methodologies, such as the Digital Twin [21], can greatly benefit from a large and structured database, especially in the context of EM CM, since faults are deeply correlated and their mechanisms are still partially obscured. This work presents a novel approach for structured data extraction from an untapped source of information, namely the knowledge stored in EM MRs in the form of NT.…”

Section: Discussionmentioning

confidence: 99%

Perspective Chapter: Data Mining in Electrical Machine Maintenance Reports

Athanasios¹,

Falekas²,

Verginadis³

et al. 2023

New Trends in Electric Machines - Technology and Applications

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Industrial electrical machine maintenance logs pertinent information, such as fault causality and earlier indications, in the form of a semi-standardized report, previously written and now in digital form. New practices in predictive maintenance, state-of-the-art condition monitoring, include increasing applications of machine learning. Reports contain a large volume of natural text in various languages and semantics, proving costly for feature extraction. This chapter aims to present novel techniques in information extraction to enable literature access to this untapped information reserve. A high level of correlation between text features and fault causality is noted, encouraging research for extended application in the scope of electrical machine maintenance, especially in artificial intelligence indication detection training. Furthermore, these innovative models can be used for decision-making during the repair. Information from well-trained classifiers can be extrapolated to advance fault causality understanding.

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Section: Discussionmentioning

confidence: 99%

Perspective Chapter: Data Mining in Electrical Machine Maintenance Reports

Athanasios¹,

Falekas²,

Verginadis³

et al. 2023

New Trends in Electric Machines - Technology and Applications

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“…With their help, it is possible to simulate optimization opportunities, conduct sensitivity analyses, or assess modification/expansion opportunities [ 120 ]. A literature review in the area of the applicability of digital twins in complex control systems was presented in the paper [ 121 ]. The digital twins’ implementation in the predictive maintenance area is reviewed in [ 122 ].…”

Section: Systematic Literature Review Of the Selected Papers Within T...mentioning

confidence: 99%

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Werbińska-Wojciechowska

Winiarska

2023

Sensors

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Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies indicate a lack of research on the systematic literature reviews and surveys of studies that would focus on the evolution of Industry 4.0 technologies used in the maintenance area in a cross-sectional manner. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology includes bibliometric performance analysis and a review of the systematic literature. First, the general bibliometric analysis was conducted based on the literature in Scopus and Web of Science databases. Later, the systematic search was performed using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2022), studies published in English, and studies found in the selected databases. In addition, the authors focused on research work within the scope of the Maintenance 4.0 study. Therefore, papers within the following research fields were selected: (a) augmented reality, (b) virtual reality, (c) system architecture, (d) data-driven decision, (e) Operator 4.0, and (f) cybersecurity. This resulted in the selection of the 214 most relevant papers in the investigated area. Finally, the selected articles in this review were categorized into five groups: (1) Data-driven decision-making in Maintenance 4.0, (2) Operator 4.0, (3) Virtual and Augmented reality in maintenance, 4) Maintenance system architecture, and 5) Cybersecurity in maintenance. The obtained results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives.

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“…In [122], a digital twin of an asynchronous machine was presented based on empirical machine data, the Robot Operating System (ROS) and the Unity 3D game engine for visualization. In [123], a literature review was presented on digital twins of electrical machines with a focus on their control and predictive maintenance.…”

Section: Permanent Magnet Synchronous Generatormentioning

confidence: 99%

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

et al. 2021

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The Industry 4.0 concept of a Digital Twin will bring many advantages for wind energy conversion systems, e.g., in condition monitoring, predictive maintenance and the optimisation of control or design parameters. A virtual replica is at the heart of a digital twin. To construct a virtual replica, appropriate modelling techniques must be selected for the turbine components. These models must be chosen with the intended use case of the digital twin in mind, finding a proper balance between the model fidelity and computational load. This review article presents an overview of the recent literature on modelling techniques for turbine aerodynamics, structure and drivetrain mechanics, the permanent magnet synchronous generator, the power electronic converter and the pitch and yaw systems. For each component, a balanced overview is given of models with varying model fidelity and computational load, ranging from simplified lumped parameter models to advanced numerical Finite Element Method (FEM)-based models. The results of the literature review are presented graphically to aid the reader in the model selection process. Based on this review, a high-level structure of a digital twin is proposed together with a virtual replica with a minimum computational load. The concept of a multi-level hierarchical virtual replica is presented.

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

Cited by 51 publications

References 62 publications

Perspective Chapter: Data Mining in Electrical Machine Maintenance Reports

Perspective Chapter: Data Mining in Electrical Machine Maintenance Reports

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

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