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Application of machine learning for inter turn fault detection in pumping system

Pump fault diagnosis is essential for the maintenance and safety of the device as it is an important appliance used in various major sectors. Fault diagnosis at the proper time can reduce maintenance costs and save energy. This article uses a Simulink model based on mathematical equations to analyze...

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Autores principales: Dutta, Nabanita, Kaliannan, Palanisamy, Shanmugam, Paramasivam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334357/
https://www.ncbi.nlm.nih.gov/pubmed/35902679
http://dx.doi.org/10.1038/s41598-022-16987-6
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author Dutta, Nabanita
Kaliannan, Palanisamy
Shanmugam, Paramasivam
author_facet Dutta, Nabanita
Kaliannan, Palanisamy
Shanmugam, Paramasivam
author_sort Dutta, Nabanita
collection PubMed
description Pump fault diagnosis is essential for the maintenance and safety of the device as it is an important appliance used in various major sectors. Fault diagnosis at the proper time can reduce maintenance costs and save energy. This article uses a Simulink model based on mathematical equations to analyze the effects of parameter estimation of three-phase induction motor-based centrifugal pumps in inter-turn fault conditions. The inter-turn fault causes a massive in, a massive increase in current, which severely affects the parameters of both motor and pump. These have been analyzed by simulation through the Matlab Simulink model. Later, the results are verified by a hardware in loop (HIL) based simulator. In this paper, machine learning (ML) based artificial neural network (ANN) and ANFIS (ANN and Fuzzy) models have been applied for fault detection. ANN and ANFIS-based models provide a satisfactory level of accuracy. These models provide accurate training and testing results. Based on root mean square error (RMSE), R(2), prediction accuracy, and mean validation value, these models are compared to find out which is more suitable for this experiment. Various supervised algorithms are compared with ANN, ANFIS, and lastly, found which is the most suitable for this experiment.
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spelling pubmed-93343572022-07-30 Application of machine learning for inter turn fault detection in pumping system Dutta, Nabanita Kaliannan, Palanisamy Shanmugam, Paramasivam Sci Rep Article Pump fault diagnosis is essential for the maintenance and safety of the device as it is an important appliance used in various major sectors. Fault diagnosis at the proper time can reduce maintenance costs and save energy. This article uses a Simulink model based on mathematical equations to analyze the effects of parameter estimation of three-phase induction motor-based centrifugal pumps in inter-turn fault conditions. The inter-turn fault causes a massive in, a massive increase in current, which severely affects the parameters of both motor and pump. These have been analyzed by simulation through the Matlab Simulink model. Later, the results are verified by a hardware in loop (HIL) based simulator. In this paper, machine learning (ML) based artificial neural network (ANN) and ANFIS (ANN and Fuzzy) models have been applied for fault detection. ANN and ANFIS-based models provide a satisfactory level of accuracy. These models provide accurate training and testing results. Based on root mean square error (RMSE), R(2), prediction accuracy, and mean validation value, these models are compared to find out which is more suitable for this experiment. Various supervised algorithms are compared with ANN, ANFIS, and lastly, found which is the most suitable for this experiment. Nature Publishing Group UK 2022-07-28 /pmc/articles/PMC9334357/ /pubmed/35902679 http://dx.doi.org/10.1038/s41598-022-16987-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Dutta, Nabanita
Kaliannan, Palanisamy
Shanmugam, Paramasivam
Application of machine learning for inter turn fault detection in pumping system
title Application of machine learning for inter turn fault detection in pumping system
title_full Application of machine learning for inter turn fault detection in pumping system
title_fullStr Application of machine learning for inter turn fault detection in pumping system
title_full_unstemmed Application of machine learning for inter turn fault detection in pumping system
title_short Application of machine learning for inter turn fault detection in pumping system
title_sort application of machine learning for inter turn fault detection in pumping system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334357/
https://www.ncbi.nlm.nih.gov/pubmed/35902679
http://dx.doi.org/10.1038/s41598-022-16987-6
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