Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157444
Title: Health monitoring of induction motor with impedance analysis and artificial intelligence
Authors: Lee, Kai Quan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: 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
Abstract: 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.
URI: https://hdl.handle.net/10356/157444
Schools: School of Electrical and Electronic Engineering 
Research Centres: Schaeffler Hub for Advanced REsearch (SHARE) Lab 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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