Signal Spectrum Based Condition Monitoring of Electrical Machines Based on Low Sampling Rate

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dc.contributor.advisorGautam, Basanta K.
dc.contributor.authorPaudel, Ashish
dc.date.accessioned2024-08-14T07:54:06Z
dc.date.available2024-08-14T07:54:06Z
dc.date.issued2024-06
dc.descriptionIn the pursuit of enhancing condition monitoring techniques for electrical machines, this research addresses the intricate challenge of sampling rates in data acquisition. Conventional high-frequency sampling, while providing rich data, incurs substantial costs in memory, processing power, and computational time.
dc.description.abstractIn the pursuit of enhancing condition monitoring techniques for electrical machines, this research addresses the intricate challenge of sampling rates in data acquisition. Conventional high-frequency sampling, while providing rich data, incurs substantial costs in memory, processing power, and computational time. Additionally, it introduces complexities related to data loss during acquisition setup. Aiming for a more balanced approach, this study explores the potential of low sampling rates, acknowledging the trade-offs in signal resolution. An innovative algorithm is proposed to harness the advantages of low sampling rates, circumventing the pitfalls of spectral leakages that often plague signal analysis. Notably, the proposed methodology eliminates the need for windowing functions, a traditional requirement in spectral analysis. The intricate process of window selection, crucial for narrowing the main lobe and reducing leakage energy, necessitates specialized knowledge. The proposed algorithm simplifies this aspect, presenting an effective solution without compromising analytical precision. This study investigates the feasibility and effectiveness of using a low sampling rate of 2 kHz for the condition monitoring of electrical machines, specifically targeting the detection of broken rotor bars (BRBs).
dc.description.provenanceSubmitted by Govind Bist (govind.bist@ncc.tu.edu.np) on 2024-08-14T07:54:06Z No. of bitstreams: 1 Ashish Paudel Master thesis electrical eng power system june 2024.pdf: 5923330 bytes, checksum: d8dff5e6455c4684749722f389f600bf (MD5)en
dc.description.provenanceMade available in DSpace on 2024-08-14T07:54:06Z (GMT). No. of bitstreams: 1 Ashish Paudel Master thesis electrical eng power system june 2024.pdf: 5923330 bytes, checksum: d8dff5e6455c4684749722f389f600bf (MD5) Previous issue date: 2024-06en
dc.identifier.urihttps://hdl.handle.net/20.500.14540/22694
dc.language.isoen
dc.publisherI.O.E
dc.subjectInduction Motor
dc.subjectFault Diagnosis
dc.subjectCondition Monitoring
dc.subjectFourier Transform
dc.titleSignal Spectrum Based Condition Monitoring of Electrical Machines Based on Low Sampling Rate
dc.typeThesis
local.academic.levelMasters
local.institute.titleInstitute of Engineering
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