Finding suitable feature extraction method for condition monitoring of electrical equipment
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021
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Brac University
2021
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| Sarrera elektronikoa: | http://hdl.handle.net/10361/15086 |
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10361-150862021-09-30T21:01:31Z Finding suitable feature extraction method for condition monitoring of electrical equipment Karobi, Synthia Hossain Rahman, Tahmidur Khoshnabish, Md Shoaib Dey, Swarup Kumar Huda, A. S. Nazmul Mohsin, Abu S.M. Department of Electrical and Electronic Engineering, Brac University Infrared Thermography Texture Analysis Co-occurrence Matrix Condition Monitoring Region of Interest Feature Extraction Auto Regression Moment Binary Gradient Level Run-length Matrix Electronic apparatus and appliances This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021 Cataloged from PDF version of thesis. Includes bibliographical references (pages 139-140). The degradation of electrical equipment caused by excessive temperature rise leading to the failure of a total electrical system can be reduced by the thermal monitoring of the equipment. Manual analysis of thermal images is time-consuming, cost-effective and can cause injuries or health damages. Therefore, building an automated fault diagnosis system plus selecting the suitable features for developing that system is essential. As there are several feature extraction methods and applying all of them to identify suitable features is time-consuming and creates extra loads on the automated system, choosing one efficient method for feature extraction is necessary. This study actually shows the comparison among different texture feature extraction techniques and find the best one by using Machine Learning. After extracting different features using different methods from thermal images of electrical equipment, firstly, supervised learning was used along with Random Forest as a classifier and then training-testing data were used to train the machine and predict the segmented regions of the pictures. The study result shows that using Gray-Level Co-Occurrence Matrix as feature extracting method gave the most accuracy and less error in the performance analysis algorithm. Finally, the condition of the electrical equipment is also predicted whether it was faulty or normal in addition to which feature extracting method provides most accuracy. Synthia Hossain Karobi Tahmidur Rahman Shoaib Khoshnabish Swarup Kumar Dey B. Electrical and Electronic Engineering 2021-09-30T13:03:50Z 2021-09-30T13:03:50Z 2021 2021-06 Thesis ID 17121049 ID 17121071 ID 17121078 ID 16221022 http://hdl.handle.net/10361/15086 en Brac University theses 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. 140 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Infrared Thermography Texture Analysis Co-occurrence Matrix Condition Monitoring Region of Interest Feature Extraction Auto Regression Moment Binary Gradient Level Run-length Matrix Electronic apparatus and appliances |
| spellingShingle |
Infrared Thermography Texture Analysis Co-occurrence Matrix Condition Monitoring Region of Interest Feature Extraction Auto Regression Moment Binary Gradient Level Run-length Matrix Electronic apparatus and appliances Karobi, Synthia Hossain Rahman, Tahmidur Khoshnabish, Md Shoaib Dey, Swarup Kumar Finding suitable feature extraction method for condition monitoring of electrical equipment |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021 |
| author2 |
Huda, A. S. Nazmul |
| author_facet |
Huda, A. S. Nazmul Karobi, Synthia Hossain Rahman, Tahmidur Khoshnabish, Md Shoaib Dey, Swarup Kumar |
| format |
Thesis |
| author |
Karobi, Synthia Hossain Rahman, Tahmidur Khoshnabish, Md Shoaib Dey, Swarup Kumar |
| author_sort |
Karobi, Synthia Hossain |
| title |
Finding suitable feature extraction method for condition monitoring of electrical equipment |
| title_short |
Finding suitable feature extraction method for condition monitoring of electrical equipment |
| title_full |
Finding suitable feature extraction method for condition monitoring of electrical equipment |
| title_fullStr |
Finding suitable feature extraction method for condition monitoring of electrical equipment |
| title_full_unstemmed |
Finding suitable feature extraction method for condition monitoring of electrical equipment |
| title_sort |
finding suitable feature extraction method for condition monitoring of electrical equipment |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15086 |
| work_keys_str_mv |
AT karobisynthiahossain findingsuitablefeatureextractionmethodforconditionmonitoringofelectricalequipment AT rahmantahmidur findingsuitablefeatureextractionmethodforconditionmonitoringofelectricalequipment AT khoshnabishmdshoaib findingsuitablefeatureextractionmethodforconditionmonitoringofelectricalequipment AT deyswarupkumar findingsuitablefeatureextractionmethodforconditionmonitoringofelectricalequipment |
| _version_ |
1814309363411582976 |