Artificial Intelligence in Monitoring and Diagnostics of Electrical Energy Conversion Systems

Vaimann, Toomas; Rassõlkin, Anton; Kallaste, Ants; Pomarnacki, Raimondas; Belahcen, Anouar; Hyunh, Van Khang

doi:10.1109/iwed48848.2020.9069566

Cited by 11 publications

(5 citation statements)

References 39 publications

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“…Signal processing is one of the essential stages in condition monitoring. Although this solution can prolong machine lifespan, this schedule-based condition monitoring approach provides very little information on the remaining useful lifetime (RUL) of the devices and does not allow for their prognostic and full exploitation [30]. Moreover, because of the scheduled controls in production, it usually means a partial or total shutdown of the manufacturing process, leading to inefficient resource usage and extra operating costs.…”

Section: Fault Signaturesmentioning

confidence: 99%

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

et al. 2021

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A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes.

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Section: Fault Signaturesmentioning

confidence: 99%

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

et al. 2021

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“…The Digital Twin (DT) [234,235], as a physical replica of a system, presents a new approach in modeling and optimization, as well as in control, maintenance, diagnostics, and many other services [236]. For maximum performance, DT needs to have a specific algorithm to be characterized in virtual assets.…”

Section: Drives and Electric Vehiclesmentioning

confidence: 99%

Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

et al. 2020

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The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.

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“…Another part of energy consumption optimization allows for more energyefficient applications of electrical energy conversion systems in a wide dynamic range of speed and power by combining the system approach with the optimization of semiconductor devices. Such a system optimization may place more stress on the separate components of the whole system in different operation modes, which can cause a higher failure rate of parts under stress [102], and more attention must be addressed to system diagnostics and maintenance. Zhang et al [103] have introduced a methodology for constrained, robust parameter optimization, which means not only mini mizing the energy consumption but also providing a set of constraints like product quality, productivity, delivery time, etc.…”

Section: Energy Optimizationmentioning

confidence: 99%

Implementation of Digital Twins for electrical energy conversion systems in selected case studies

Demidova¹,

Kallaste²,

Kuts³

et al. 2021

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Reference implementation of Digital Twins for electrical energy conversion systems is an important and open question in the industrial domain. Digital Twins can predict the future performance, behaviour, and maintenance needs of a complex system. Today the concept of Digital Twins is not only an emulation or simulation of the physical object along with its development history but also contains much information from the respective manufacturers and services. This paper presents the current stateoftheart of Digital Twins in relation to some interesting novel applications from different fields of electrical engineering. The objective of the paper is to give an overview of the successful application of Digital Twins in electrical energy conversion systems, such as industrial robotics and wind turbines; to discuss trends in applications like electric vehicles; and to suggest new applications, such as telescopes. Special attention is paid to the possible application of Digital Twins in faults diagnostics and prognostics of electrical energy conversion systems. Successful implementation of Digital Twins in any electrical energy conversion system diagnostics and prognostics allows for lowcost maintenance, higher utilization of the individual devices and systems, as well as lower usage of material and human resources. A SWOT analysis was performed for Digital Twin applications in electrical energy conversion systems. The latter is a useful analysis technique that explores possibilities for new achievements or solutions to existing problems and makes decisions about the best path.

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Artificial Intelligence in Monitoring and Diagnostics of Electrical Energy Conversion Systems

Cited by 11 publications

References 39 publications

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

Implementation of Digital Twins for electrical energy conversion systems in selected case studies

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