Design of Vibration Frequency Method with Fine-Tuned Factor for Fault Detection of Three Phase Induction Motor

Author:

Amanuel Thomas,Ghirmay Amanuel,Ghebremeskel Huruy,Ghebrehiwet Robel,Bahlibi Weldekidan

Abstract

This research article focuses on industrial applications to demonstrate the characterization of current and vibration analysis to diagnose the induction motor drive problems. Generally, the induction motor faults are detected by monitoring the current and proposed fine-tuned vibration frequency method. The stator short circuit fault, broken rotor bar fault, air gap eccentricity, and bearing fault are the common faults that occur in an induction motor. The detection process of the proposed method is based on sidebands around the supply frequency in the stator current signal and vibration. Moreover, it is very challenging to diagnose the problem that occur due to the complex electromagnetic and mechanical characteristics of an induction motor with vibration measures. The design of an accurate model to measure vibration and stator current is analyzed in this research article. The proposed method is showing how efficiently the root cause of the problem can be diagnosed by using the combination of current and vibration monitoring method. The proposed model is developed for induction motor and its circuit environment in MATLAB is verified to perform an accurate detection and diagnosis of motor fault parameters. All stator faults are turned to turn fault; further, the rotor-broken bar and eccentricity are structured in each test. The output response (torque and stator current) is simulated by using a modified winding procedure (MWP) approach by tuning the winding geometrical parameter. The proposed model in MATLAB Simulink environment is highly symmetrical, which can easily detect the signal component in fault frequencies that occur due to a slight variation and improper motor installation. Finally, this research article compares the other existing methods with proposed method.

Publisher

Inventive Research Organization

Cited by 39 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Robust Fault Diagnostics of Industrial Motors: The Signal-to-Image Approaches for Sensor Data Using Multi-Task Transformers with Diverse Attention Mechanism;2024 IEEE 3rd International Conference on Control, Instrumentation, Energy & Communication (CIEC);2024-01-25

2. The Application of Three-Phase Induction Motor Protection Automated System To Overcome Overload Conditions and Zelio Logic-Based High Temperatures;2023 7th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM);2023-12-13

3. Revolutionizing Motor Health: IoT-Driven Detection of Electrical Abnormalities in Three-Phase A.C. Induction Motors;Malaysian Journal of Science and Advanced Technology;2023-12-04

4. A Reliable Neural Network Approach for Monitoring and Diagnosing Bearing Faults in Induction Motors;2023 International Conference on Decision Aid Sciences and Applications (DASA);2023-09-16

5. Enhanced solar systems efficiency and reduce energy waste by using IoT devices;Materials Today: Proceedings;2023-06

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3