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DC Motor Control Technology Based on Multisensor Information Fusion

To solve these uncertain problems by studying the motor fault diagnosis technology, so as to ensure the normal operation of the motor equipment is the primary problem to be solved in the field of motor fault diagnosis. The traditional DC motor is one of the most widely used motors at present. It has...

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Detalles Bibliográficos
Autor principal: Lu, Yean
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270143/
https://www.ncbi.nlm.nih.gov/pubmed/35814552
http://dx.doi.org/10.1155/2022/1447333
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author Lu, Yean
author_facet Lu, Yean
author_sort Lu, Yean
collection PubMed
description To solve these uncertain problems by studying the motor fault diagnosis technology, so as to ensure the normal operation of the motor equipment is the primary problem to be solved in the field of motor fault diagnosis. The traditional DC motor is one of the most widely used motors at present. It has excellent speed regulation performance and is easy to control. It is widely used in applications that require high motor startup and speed regulation characteristics. This research mainly discusses DC motor control technology. Evidence theory can combine various fault information at different levels to enhance mutual support between pieces of evidence, thereby improving the accuracy of motor fault detection. Based on the steps of signal processing, feature extraction, feature dimensionality reduction, and state recognition, the research on the state recognition method of belt conveyor drive motor based on multisource information fusion is carried out. By studying the multisource information fusion, this paper proposes a two-stage belt conveyor drive motor information fusion model based on the optimal D-S evidence theory. The correct identification rate of broken rotor bars during fault monitoring is 99.8%. This method divides the specific motor fault feature set into multiple fault subspaces and uses different diagnostic neural networks and different fault feature parameters for local diagnosis, respectively. The scheme designed in this study significantly improves the recognition accuracy of the motor in the same working condition and under variable working conditions. The drive motor state recognition and intelligent decision-making system designed by combining the results of multisource information fusion can effectively describe the fault type and has strong operability.
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spelling pubmed-92701432022-07-09 DC Motor Control Technology Based on Multisensor Information Fusion Lu, Yean Comput Intell Neurosci Research Article To solve these uncertain problems by studying the motor fault diagnosis technology, so as to ensure the normal operation of the motor equipment is the primary problem to be solved in the field of motor fault diagnosis. The traditional DC motor is one of the most widely used motors at present. It has excellent speed regulation performance and is easy to control. It is widely used in applications that require high motor startup and speed regulation characteristics. This research mainly discusses DC motor control technology. Evidence theory can combine various fault information at different levels to enhance mutual support between pieces of evidence, thereby improving the accuracy of motor fault detection. Based on the steps of signal processing, feature extraction, feature dimensionality reduction, and state recognition, the research on the state recognition method of belt conveyor drive motor based on multisource information fusion is carried out. By studying the multisource information fusion, this paper proposes a two-stage belt conveyor drive motor information fusion model based on the optimal D-S evidence theory. The correct identification rate of broken rotor bars during fault monitoring is 99.8%. This method divides the specific motor fault feature set into multiple fault subspaces and uses different diagnostic neural networks and different fault feature parameters for local diagnosis, respectively. The scheme designed in this study significantly improves the recognition accuracy of the motor in the same working condition and under variable working conditions. The drive motor state recognition and intelligent decision-making system designed by combining the results of multisource information fusion can effectively describe the fault type and has strong operability. Hindawi 2022-07-01 /pmc/articles/PMC9270143/ /pubmed/35814552 http://dx.doi.org/10.1155/2022/1447333 Text en Copyright © 2022 Yean Lu. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Lu, Yean
DC Motor Control Technology Based on Multisensor Information Fusion
title DC Motor Control Technology Based on Multisensor Information Fusion
title_full DC Motor Control Technology Based on Multisensor Information Fusion
title_fullStr DC Motor Control Technology Based on Multisensor Information Fusion
title_full_unstemmed DC Motor Control Technology Based on Multisensor Information Fusion
title_short DC Motor Control Technology Based on Multisensor Information Fusion
title_sort dc motor control technology based on multisensor information fusion
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270143/
https://www.ncbi.nlm.nih.gov/pubmed/35814552
http://dx.doi.org/10.1155/2022/1447333
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