Investigation of the Effectiveness of Oscillation Control in a Three-Mass System Based on the Additional Feedbacks

Kuznetsov, N. K.; Dolgih, E. S.; Iov, I A

doi:10.2991/aviaent-19.2019.40

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…In a number of studies, an attempt has been made to develop intelligent electric drive control systems that prevent possible malfunctions [22][23][24], which, however, cannot be applied for the comprehensive solution of the problems that arise during the ore crushing process. Baghdasaryan & Avetisyan [25] discussed the issues of the stability of the motion of the "electric motortechnological load" system.…”

Section: Introductionmentioning

confidence: 99%

Stability Assessment of an Ore Mill Electric Drive Using Machine Learning

Baghdasaryan,

Hovhannisyn

2024

HighTech. Innov. J.

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The relevance of the study is due to the need to improve electric drive systems operated in harsh conditions. The goal of the study is to create a model for assessing the state of stability of the electric drive of an ore mill using machine learning capabilities, which will provide high performance and the ability to work consistently in different systems. Various sustainability assessment models have been developed based on 6 machine learning algorithms. The study and comparison of models built using artificial neural networks (ANN) of different architectures was carried out using various learning methods. The expediency of using the Tree and ANN algorithms to develop a model for assessing electric drive stability is substantiated. The novelty of the results obtained lies in the fact that the model has high accuracy, high speed, and the ability to detect instability in uncertain operating modes of the electric motor of an electric drive, as well as the possibility of coordinated operation with various systems. The practical value is that the model allows, at an intellectual level, to provide effective control and fault diagnosis of complex electric drive systems, which cannot be achieved using the known methods. Doi: 10.28991/HIJ-2024-05-02-01 Full Text: PDF

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Section: Introductionmentioning

confidence: 99%

Stability Assessment of an Ore Mill Electric Drive Using Machine Learning

Baghdasaryan,

Hovhannisyn

2024

HighTech. Innov. J.

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On the Suppression of Vibrations in Elastic Links of a Threemass Electric Drive System

Baghdasaryan,

Davtyan,

Alavedyan

2024

Proceedings of National Polytechnic University of Armenia. INFORMATION TECHNOLOGIES, ELECTRONICS, RADIO ENGINEERING

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The main reason for failures of technological mechanisms is associated with the failure of elements of the electric drive system. One of the main reasons for the malfunction of electric drive elements is the presence of vibrations in the elastic links of the mechanical part of the electric drive. Such phenomena mainly occur during transient processes of starting and stopping an electric drive, as well as under the influence of a load that is variable in nature. From the above, it is obvious that suppression of vibrations in the elastic links of the mechanical part is of practical interest. The most promising studies aimed at studying and suppressing vibrations in the elastic links of a three-mass electric drive system are analyzed. A comparative analysis of these studies shows that they consider the operating features of individual specific electric drive systems, which cannot provide a complete understanding of a comprehensive solution to the problem of ensuring the suppression of vibrations, arising in the mechanical part for various reasons. That is why it is necessary to evaluate the possibilities of suppressing elastic vibrations of the electric drive system, taking into account all its links. The goal of this work is to propose an approach for suppressing vibrations in the elastic links of a three-mass electric drive system. The change in the elastic moment under various characteristics of the load created by the mechanism is considered, in particular, in cases of constant moment, exponential and step laws of change. The capabilities of proportional-integral (PI) and proportional-integral-derivative (PID) controllers for suppressing oscillations are analyzed. It is substantiated that although the system stabilizes with a PID controller in a short time, it does not provide the expected result and therefore it is recommended to use a controller with a fuzzy logic due to its simple control and rather high efficiency. The results obtained can be successfully used to improve the reliability of a three-mass electric drive system.

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Investigation of the Effectiveness of Oscillation Control in a Three-Mass System Based on the Additional Feedbacks

Cited by 2 publications

References 2 publications

Stability Assessment of an Ore Mill Electric Drive Using Machine Learning

Stability Assessment of an Ore Mill Electric Drive Using Machine Learning

On the Suppression of Vibrations in Elastic Links of a Threemass Electric Drive System

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