Abnormal Health Monitoring and Assessment of a Three-Phase Induction Motor Using a Supervised CNN-RNN-Based Machine Learning Algorithm

被引：1

作者：

Saxena A. ^{[1
]}

Kumar R. ^{[2
]}

Rawat A.K. ^{[1
]}

Majid M. ^{[3
]}

Singh J. ^{[4
]}

Devakirubakaran S. ^{[5
]}

Singh G.K. ^{[6
]}

机构：

[1] Department of Electrical Engineering, JSS Academy of Technical Education, Uttar Pradesh, Noida

[2] Department of Electrical Engineering, Dayalbagh Educational Institute, Dayalbagh, Uttar Pradesh, Agra

[3] Department of Mechanical Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, Sangrur

[4] Department of Electrical and Electronics Engineering, GL Bajaj Institute of Technology and Management, Uttar Pradesh, Noida

[5] Dapartment of Electrical and Electronics Engineering, QIS College of Engineering and Technology, Andhra Pradesh, Ongole

[6] School of Mechanical, Chemical and Materials Engineering, Adama Science and Technology University, Adama

来源：

Mathematical Problems in Engineering | 2023年 / 2023卷

关键词：

Electric grounding - Fuzzy logic - Fuzzy neural networks - Induction motors - Learning algorithms;

D O I：

10.1155/2023/1264345

中图分类号：

学科分类号：

摘要：

This paper shows the health monitoring and assessment of a three-phase induction motor in abnormal conditions using a machine learning algorithm. The convolutional neural network (CNN) and recurrent neural network (RNN) algorithms are the prominent methods used in machine learning algorithms, and the combined method is known as the CRNN method. The abnormal conditions of a three phase-induction motor are represented by three-phase faults, line-to-ground faults, etc. The pattern of fault current is traced, and key features are extracted by the CRNN algorithm. The performance parameters like THD (%), accuracy, and reliability of abnormal conditions are measured with the CRNN algorithm. The assessment of abnormal conditions is being realized at the terminals of a three-phase induction motor. A fuzzy logic controller (FLC) is also used to assess such abnormalities. It is observed that performance parameters are found to be better with the CRNN method in comparison to CNN, RNN, ANN, and other methods. Such a realization makes the system more compatible with abnormality recognition. © 2023 Abhinav Saxena et al.

引用

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