Neural network based incipient fault detection of induction motors
Thesis (M.Eng.)--Memorial University of Newfoundland, 1995. Engineering and Applied Science Bibliography: leaves 120-123. An incipient fault detection scheme of induction motors through the recognition of frequency spectra of the stator current has been developed in this thesis. It is based on the a...
Main Author: | |
---|---|
Other Authors: | |
Format: | Thesis |
Language: | English |
Published: |
1995
|
Subjects: | |
Online Access: | http://collections.mun.ca/cdm/ref/collection/theses2/id/203591 |
id |
ftmemorialunivdc:oai:collections.mun.ca:theses2/203591 |
---|---|
record_format |
openpolar |
spelling |
ftmemorialunivdc:oai:collections.mun.ca:theses2/203591 2023-05-15T17:23:34+02:00 Neural network based incipient fault detection of induction motors Rokonuzzaman, Mohd., 1965- Memorial University of Newfoundland. Faculty of Engineering and Applied Science 1995 xviii, 161 leaves : ill. Image/jpeg; Application/pdf http://collections.mun.ca/cdm/ref/collection/theses2/id/203591 Eng eng Electronic Theses and Dissertations (15.60 MB) -- http://collections.mun.ca/PDFs/theses/Rokonuzzaman_Mohd2.pdf 76245927 http://collections.mun.ca/cdm/ref/collection/theses2/id/203591 The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries Electric motors Induction Fault location (Engineering) Neural networks (Computer science) Text Electronic thesis or dissertation 1995 ftmemorialunivdc 2015-08-06T19:17:16Z Thesis (M.Eng.)--Memorial University of Newfoundland, 1995. Engineering and Applied Science Bibliography: leaves 120-123. An incipient fault detection scheme of induction motors through the recognition of frequency spectra of the stator current has been developed in this thesis. It is based on the adaptive resonance theory of neural networks. This fault diagnosis scheme is not only capable of detecting a fault but also can report if it cannot identify a particular fault so that necessary preventive steps can be taken to update the underlying neural network to adapt to this undetected fault. Moreover, it can update itself to cope with this dynamic situation retaining already acquired knowledge without the need of retraining with the old patterns. -- A laboratory experimental set-up using a digital signal processing(DSP) technique has been employed to collect the frequency spectra of the stator current at different fault conditions. A wound-rotor induction motor has been used as the test motor to create different types of faults making unbalance in the stator and rotor circuits. A 24-bit high speed DSP board has been used with a personal computer to develop a real-time interactive software to collect the spectra. A driver for the HP-plotter has also been developed to directly plot the frequency spectra of the stator current. -- Adaptive resonance theory(ART) based network is a recent addition to the neural network family. A new software has been successfully developed and implemented in the laboratory experiment using ART neural network. Its performances in training, recalling and dynamic updating have been studied with a set of example patterns. The incipient faults of a 3-phase wound rotor induction motor have been successfully diagonized by this neural network. Thesis Newfoundland studies University of Newfoundland Memorial University of Newfoundland: Digital Archives Initiative (DAI) |
institution |
Open Polar |
collection |
Memorial University of Newfoundland: Digital Archives Initiative (DAI) |
op_collection_id |
ftmemorialunivdc |
language |
English |
topic |
Electric motors Induction Fault location (Engineering) Neural networks (Computer science) |
spellingShingle |
Electric motors Induction Fault location (Engineering) Neural networks (Computer science) Rokonuzzaman, Mohd., 1965- Neural network based incipient fault detection of induction motors |
topic_facet |
Electric motors Induction Fault location (Engineering) Neural networks (Computer science) |
description |
Thesis (M.Eng.)--Memorial University of Newfoundland, 1995. Engineering and Applied Science Bibliography: leaves 120-123. An incipient fault detection scheme of induction motors through the recognition of frequency spectra of the stator current has been developed in this thesis. It is based on the adaptive resonance theory of neural networks. This fault diagnosis scheme is not only capable of detecting a fault but also can report if it cannot identify a particular fault so that necessary preventive steps can be taken to update the underlying neural network to adapt to this undetected fault. Moreover, it can update itself to cope with this dynamic situation retaining already acquired knowledge without the need of retraining with the old patterns. -- A laboratory experimental set-up using a digital signal processing(DSP) technique has been employed to collect the frequency spectra of the stator current at different fault conditions. A wound-rotor induction motor has been used as the test motor to create different types of faults making unbalance in the stator and rotor circuits. A 24-bit high speed DSP board has been used with a personal computer to develop a real-time interactive software to collect the spectra. A driver for the HP-plotter has also been developed to directly plot the frequency spectra of the stator current. -- Adaptive resonance theory(ART) based network is a recent addition to the neural network family. A new software has been successfully developed and implemented in the laboratory experiment using ART neural network. Its performances in training, recalling and dynamic updating have been studied with a set of example patterns. The incipient faults of a 3-phase wound rotor induction motor have been successfully diagonized by this neural network. |
author2 |
Memorial University of Newfoundland. Faculty of Engineering and Applied Science |
format |
Thesis |
author |
Rokonuzzaman, Mohd., 1965- |
author_facet |
Rokonuzzaman, Mohd., 1965- |
author_sort |
Rokonuzzaman, Mohd., 1965- |
title |
Neural network based incipient fault detection of induction motors |
title_short |
Neural network based incipient fault detection of induction motors |
title_full |
Neural network based incipient fault detection of induction motors |
title_fullStr |
Neural network based incipient fault detection of induction motors |
title_full_unstemmed |
Neural network based incipient fault detection of induction motors |
title_sort |
neural network based incipient fault detection of induction motors |
publishDate |
1995 |
url |
http://collections.mun.ca/cdm/ref/collection/theses2/id/203591 |
genre |
Newfoundland studies University of Newfoundland |
genre_facet |
Newfoundland studies University of Newfoundland |
op_source |
Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries |
op_relation |
Electronic Theses and Dissertations (15.60 MB) -- http://collections.mun.ca/PDFs/theses/Rokonuzzaman_Mohd2.pdf 76245927 http://collections.mun.ca/cdm/ref/collection/theses2/id/203591 |
op_rights |
The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. |
_version_ |
1766113408236650496 |