Analysis of Effects of Loading Conditions on Condition Monitoring of Induction Machines

Simple item page
dc.contributor.advisorKarki, Nawaraj
dc.contributor.authorPokhrel, Chandan
dc.date.accessioned2024-08-14T09:26:16Z
dc.date.available2024-08-14T09:26:16Z
dc.date.issued2024-06
dc.descriptionThis study specifically focuses on analyzing the stator current signals of a squirrel cage induction machine to detect and diagnose broken rotor bar faults under different load conditions. The research employs a multi-layer perceptron (MLP) model, a type of artificial neural network known for its ability to learn complex patterns, to study the impact of varying operational loads on fault detection accuracy.
dc.description.abstracteffectiveness, robustness, and high efficiency. However, as with all electrical machines, they are prone to degradation over time, necessitating expensive repairs and maintenance. Detecting faults early is critical to minimizing unplanned downtime and reducing operational costs. One significant fault that can significantly impact the performance of induction machines is broken rotor bars. Broken rotor bars can lead to various undesirable effects, including increased vibration, reduced efficiency, and potential catastrophic failure if left undetected. Motor current signature analysis (MCSA) is a widely adopted technique for monitoring the condition of induction machines during normal operation. By analyzing the frequency spectrum of the stator current, MCSA provides valuable insights into the health of the machine and can detect anomalies indicative of broken rotor bars. This study specifically focuses on analyzing the stator current signals of a squirrel cage induction machine to detect and diagnose broken rotor bar faults under different load conditions. The research employs a multi-layer perceptron (MLP) model, a type of artificial neural network known for its ability to learn complex patterns, to study the impact of varying operational loads on fault detection accuracy.
dc.description.provenanceSubmitted by Govind Bist (govind.bist@ncc.tu.edu.np) on 2024-08-14T09:26:16Z No. of bitstreams: 1 Chandan Pokhrel Master thesis electrical power system july 2024.pdf: 5894676 bytes, checksum: 71874ee5162b7397ca29c4014ddbe6dd (MD5)en
dc.description.provenanceMade available in DSpace on 2024-08-14T09:26:16Z (GMT). No. of bitstreams: 1 Chandan Pokhrel Master thesis electrical power system july 2024.pdf: 5894676 bytes, checksum: 71874ee5162b7397ca29c4014ddbe6dd (MD5) Previous issue date: 2024-06en
dc.identifier.urihttps://hdl.handle.net/20.500.14540/22696
dc.language.isoen
dc.publisherI.O.E
dc.subjectInduction Motor
dc.subjectCondition Monitoring
dc.subjectFourier Transform
dc.subjectBroken Rotor Bars
dc.titleAnalysis of Effects of Loading Conditions on Condition Monitoring of Induction Machines
dc.typeThesis
local.academic.levelMasters
local.institute.titleInstitute of Engineering
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Chandan Pokhrel Master thesis electrical power system july 2024.pdf
Size:
5.62 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Power System Engineering