Single Phase Fault Detection of Induction Motor using Machine Learning Approaches-Reference-Cited by-同舟云学术

Single Phase Fault Detection of Induction Motor using Machine Learning Approaches

Published:2024-03-04 Issue: Volume: Page:122-127
ISSN:
Container-title:2024 IEEE 4th International Conference in Power Engineering Applications (ICPEA)
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Author:

Turza Udoy Roy¹,Das Viswya Deb¹,Islam Shaikh Hasib-Ul¹,Pathan Mih¹,Shahriar Mohammad Shoaib²,Alam Md. Shafiul³,Rahman Md. Mizanur¹,Islam Khondoker Ziaul⁴,Hossain Md. Sanwar⁵,Rahman Mohammad Mominur⁶

Affiliation:

1. Hajee Mohammad Danesh Science and Technology University,Electrical and Electronic Engineering,Basherhat,Bangladesh

2. University of Hafr Al Batin,Electrical Engineering,Hafar Al Batin,Saudi Arabia

3. King Fahd University of Petroleum & Minerals,Environment and Marine Studies,Dhahran,Saudi Arabia

4. Murdoch University,Information Technology,Perth,Australia

5. University of Wollongong,School of Electrical, Computer and Telecommunication Engineering,Wollongong,NSW,Australia

6. Hamad Bin Khalifa University,College of Science and Engineering,Ar-Rayyan,Qatar

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10498129/10498123/10498946.pdf?arnumber=10498946

Reference21 articles.

1. Fault diagnosis of single-phase induction motor based on acoustic signals

2. Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection

3. Method for Identifying Stator and Rotor Faults of Induction Motors Based on Machine Vision

4. Condition Monitoring and Fault Diagnosis of Induction Motors: A Review

5. Induction motor broken rotor bar fault detection techniques based on fault signature analysis – a review