NEURAL NETWORK APPLIED FOR THE FAULT DIAGNOSIS OF AN AC MOTOR

There are many failures of AC motors in the industry for different reasons and huge losses are affected. This failure takes some time to happen and the cause slowly affects the motors. In this project report, the neural network comes with a solution to the problem. The neural networks able to dia...

Full description

Saved in:
Bibliographic Details
Main Author: Yahya, Muhammad Ariff
Format: Final Year Project
Language:English
Published: Universiti Teknologi PETRONAS 2007
Subjects:
Online Access:http://utpedia.utp.edu.my/9699/1/2007%20-Neural%20Network%20Applied%20For%20The%20Fault%20Diagnosis%20Of%20An%20AC%20Motor.pdf
http://utpedia.utp.edu.my/9699/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-utp-utpedia.9699
record_format eprints
spelling my-utp-utpedia.96992017-01-25T09:45:55Z http://utpedia.utp.edu.my/9699/ NEURAL NETWORK APPLIED FOR THE FAULT DIAGNOSIS OF AN AC MOTOR Yahya, Muhammad Ariff TK Electrical engineering. Electronics Nuclear engineering There are many failures of AC motors in the industry for different reasons and huge losses are affected. This failure takes some time to happen and the cause slowly affects the motors. In this project report, the neural network comes with a solution to the problem. The neural networks able to diagnosis the incipient AC motors faults. The network collects all the possible causes to any failure and analyzes it with existing trained datato determine the statusof the motor. For example if the motorhaving degradation on the winding insulation resistance, the network will collect the status of the insulation resistance and compare it with the allowable value of the insulation resistance, thus the output states the future failure with the current value ofthe insulation resistance on the motor's winding. This project report covers onthree fault outputs which are bearing fault, winding fault and overheating fault. All these outputs depend on certain inputs where the inputs are the cause towards the failure. The artificial neural network has various types of usage. For the prediction purposes, feedfoward-back propagation network topology will be used. This topology will be is with appropriate training function. The training function for example, Resilient-Backpropagation will be trained and provides values for weights and biases of the network topology. The biases and weights are used to analyze any input fed to this network and come out with its prediction. Furthermore the project report also deals with MATLAB simulation and toolbox. TheMATLAB software is able to tolerate the neural networks where all the command and application are in MATLAB based. Universiti Teknologi PETRONAS 2007-06 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/9699/1/2007%20-Neural%20Network%20Applied%20For%20The%20Fault%20Diagnosis%20Of%20An%20AC%20Motor.pdf Yahya, Muhammad Ariff (2007) NEURAL NETWORK APPLIED FOR THE FAULT DIAGNOSIS OF AN AC MOTOR. Universiti Teknologi PETRONAS. (Unpublished)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Yahya, Muhammad Ariff
NEURAL NETWORK APPLIED FOR THE FAULT DIAGNOSIS OF AN AC MOTOR
description There are many failures of AC motors in the industry for different reasons and huge losses are affected. This failure takes some time to happen and the cause slowly affects the motors. In this project report, the neural network comes with a solution to the problem. The neural networks able to diagnosis the incipient AC motors faults. The network collects all the possible causes to any failure and analyzes it with existing trained datato determine the statusof the motor. For example if the motorhaving degradation on the winding insulation resistance, the network will collect the status of the insulation resistance and compare it with the allowable value of the insulation resistance, thus the output states the future failure with the current value ofthe insulation resistance on the motor's winding. This project report covers onthree fault outputs which are bearing fault, winding fault and overheating fault. All these outputs depend on certain inputs where the inputs are the cause towards the failure. The artificial neural network has various types of usage. For the prediction purposes, feedfoward-back propagation network topology will be used. This topology will be is with appropriate training function. The training function for example, Resilient-Backpropagation will be trained and provides values for weights and biases of the network topology. The biases and weights are used to analyze any input fed to this network and come out with its prediction. Furthermore the project report also deals with MATLAB simulation and toolbox. TheMATLAB software is able to tolerate the neural networks where all the command and application are in MATLAB based.
format Final Year Project
author Yahya, Muhammad Ariff
author_facet Yahya, Muhammad Ariff
author_sort Yahya, Muhammad Ariff
title NEURAL NETWORK APPLIED FOR THE FAULT DIAGNOSIS OF AN AC MOTOR
title_short NEURAL NETWORK APPLIED FOR THE FAULT DIAGNOSIS OF AN AC MOTOR
title_full NEURAL NETWORK APPLIED FOR THE FAULT DIAGNOSIS OF AN AC MOTOR
title_fullStr NEURAL NETWORK APPLIED FOR THE FAULT DIAGNOSIS OF AN AC MOTOR
title_full_unstemmed NEURAL NETWORK APPLIED FOR THE FAULT DIAGNOSIS OF AN AC MOTOR
title_sort neural network applied for the fault diagnosis of an ac motor
publisher Universiti Teknologi PETRONAS
publishDate 2007
url http://utpedia.utp.edu.my/9699/1/2007%20-Neural%20Network%20Applied%20For%20The%20Fault%20Diagnosis%20Of%20An%20AC%20Motor.pdf
http://utpedia.utp.edu.my/9699/
_version_ 1739831706100170752
score 13.1944895