Comparative study of 1D de-noising techniques using induction motor fault signals

Comparative study of 1D de-noising techniques using induction motor fault signals

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.

Bibliografski detalji
Glavni autori: Lamia, Rehnuma Tasnim, Iqbal, Zafor
Daljnji autori: Uddin, Dr. Jia
Format: Disertacija
Jezik:English
Izdano: BRAC University 2016
Teme:
Induction motor
Low pass filter
Cross correlation
Online pristup:http://hdl.handle.net/10361/6374
id 10361-6374
record_format dspace
spelling 10361-63742022-01-26T10:08:21Z Comparative study of 1D de-noising techniques using induction motor fault signals Lamia, Rehnuma Tasnim Iqbal, Zafor Uddin, Dr. Jia Department of Computer Science and Engineering, BRAC University Induction motor Low pass filter Cross correlation This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016. Cataloged from PDF version of thesis report. Includes bibliographical references (page 43-45). Removing noise from the original signals has always been challenging. A lot of denoising techniques and algorithms has already been published and are being used now-a-days. But knowing which de-noising method is better we need to compare the performance of their applications so that a new group of people can start working instantly knowing which method is better than the other considering specific parameters. In this thesis, we will compare the de-noising techniques using discrete wavelet transform (DWT), empirical mode decomposition (EMD), Gabor filter, butter filter, low pass filter, high pass filter, band stop filter, Hilbert filter, Median filter and Q function. To evaluate the performance of the state-of-art models we will utilize an induction motor dataset that consist of inner, outer, and roller fault signals including healthy/normal signal. Finally, the performance will be measured using the following parameters: signal to noise ratio (SNR), peak signal to noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). We will also find the structural similarity(SSIM), structural dissimilarity(DSSIM) and cross correlation (CC) between the parameter. Rehnuma Tasnim Lamia Zafor Iqbal B. Computer Science and Engineering 2016-09-07T05:09:40Z 2016-09-07T05:09:40Z 2016 2016-08 Thesis ID 10201012 ID 14341015 http://hdl.handle.net/10361/6374 en BRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 45 pages application/pdf BRAC University
institution Brac University
collection Institutional Repository
language English
topic Induction motor
Low pass filter
Cross correlation
spellingShingle Induction motor
Low pass filter
Cross correlation
Lamia, Rehnuma Tasnim
Iqbal, Zafor
Comparative study of 1D de-noising techniques using induction motor fault signals
description This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.
author2 Uddin, Dr. Jia
author_facet Uddin, Dr. Jia
Lamia, Rehnuma Tasnim
Iqbal, Zafor
format Thesis
author Lamia, Rehnuma Tasnim
Iqbal, Zafor
author_sort Lamia, Rehnuma Tasnim
title Comparative study of 1D de-noising techniques using induction motor fault signals
title_short Comparative study of 1D de-noising techniques using induction motor fault signals
title_full Comparative study of 1D de-noising techniques using induction motor fault signals
title_fullStr Comparative study of 1D de-noising techniques using induction motor fault signals
title_full_unstemmed Comparative study of 1D de-noising techniques using induction motor fault signals
title_sort comparative study of 1d de-noising techniques using induction motor fault signals
publisher BRAC University
publishDate 2016
url http://hdl.handle.net/10361/6374
work_keys_str_mv AT lamiarehnumatasnim comparativestudyof1ddenoisingtechniquesusinginductionmotorfaultsignals
AT iqbalzafor comparativestudyof1ddenoisingtechniquesusinginductionmotorfaultsignals
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