Health monitoring of induction motor with impedance analysis and artificial intelligence
The induction motor is utilized in almost all technological applications, and it is known as one of the industry’s workhorses. In every system, it is critical to ensure that the induction motor operates safely and reliably to prevent any personnel from dangerous hazards. The stator winding fa...
| Main Author: | |
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/157444 |
| _version_ | 1824454971237597184 |
|---|---|
| author | Lee, Kai Quan |
| author2 | Cai Wenjian |
| author_facet | Cai Wenjian Lee, Kai Quan |
| author_sort | Lee, Kai Quan |
| collection | NTU |
| description | The induction motor is utilized in almost all technological applications, and it is known as
one of the industry’s workhorses. In every system, it is critical to ensure that the induction
motor operates safely and reliably to prevent any personnel from dangerous hazards.
The stator winding faults of the induction motor are one of the most common faults that can
happen. As a result, implementing and utilizing new technology like Artificial Intelligence to
monitor for any early stage of defects within the induction motor will be beneficial in
providing timely maintenance and condition monitoring.
The proposed technique for detecting stator winding faults in induction motors discussed in
this study is a non-intrusive Machine Learning method. The early stages of any stator
winding faults can be detected by using frequency and impedance magnitude data as my main
parameters. As a result, possible dangers will be eliminated, motor downtime will be reduced,
and maintenance costs are reduced as well.
The reliability and accuracy of the proposed method will be proven by the experimental
results of my Neural Network Model. |
| first_indexed | 2025-02-19T03:30:47Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157444 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:30:47Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1574442023-07-07T19:18:38Z Health monitoring of induction motor with impedance analysis and artificial intelligence Lee, Kai Quan Cai Wenjian See Kye Yak School of Electrical and Electronic Engineering Schaeffler Hub for Advanced REsearch (SHARE) Lab EKYSEE@ntu.edu.sg, ewjcai@ntu.edu.sg Engineering::Electrical and electronic engineering The induction motor is utilized in almost all technological applications, and it is known as one of the industry’s workhorses. In every system, it is critical to ensure that the induction motor operates safely and reliably to prevent any personnel from dangerous hazards. The stator winding faults of the induction motor are one of the most common faults that can happen. As a result, implementing and utilizing new technology like Artificial Intelligence to monitor for any early stage of defects within the induction motor will be beneficial in providing timely maintenance and condition monitoring. The proposed technique for detecting stator winding faults in induction motors discussed in this study is a non-intrusive Machine Learning method. The early stages of any stator winding faults can be detected by using frequency and impedance magnitude data as my main parameters. As a result, possible dangers will be eliminated, motor downtime will be reduced, and maintenance costs are reduced as well. The reliability and accuracy of the proposed method will be proven by the experimental results of my Neural Network Model. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-16T11:56:25Z 2022-05-16T11:56:25Z 2022 Final Year Project (FYP) Lee, K. Q. (2022). Health monitoring of induction motor with impedance analysis and artificial intelligence. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157444 https://hdl.handle.net/10356/157444 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Lee, Kai Quan Health monitoring of induction motor with impedance analysis and artificial intelligence |
| title | Health monitoring of induction motor with impedance analysis and artificial intelligence |
| title_full | Health monitoring of induction motor with impedance analysis and artificial intelligence |
| title_fullStr | Health monitoring of induction motor with impedance analysis and artificial intelligence |
| title_full_unstemmed | Health monitoring of induction motor with impedance analysis and artificial intelligence |
| title_short | Health monitoring of induction motor with impedance analysis and artificial intelligence |
| title_sort | health monitoring of induction motor with impedance analysis and artificial intelligence |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/157444 |
| work_keys_str_mv | AT leekaiquan healthmonitoringofinductionmotorwithimpedanceanalysisandartificialintelligence |