A texture based industrial fault diagnosis model using Gammatone Filter Bank and transfer learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

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Hlavní autoři:	Morshed, Atique, Siraj, Farhan Md., Munim, Abu Sadat MD., Ayon, Tasnimul Karim, Goni, Saad Rafsan
Další autoři:	Uddin, Jia
Médium:	Diplomová práce
Jazyk:	English
Vydáno:	Brac University 2021
Témata:	Deep learning Transfer learning Fault detection RNN LSTM Gammatone Filter Bank Artificial intelligence Cognitive learning theory
On-line přístup:	http://hdl.handle.net/10361/15759

id	10361-15759
record_format	dspace
spelling	10361-157592022-01-26T10:08:20Z A texture based industrial fault diagnosis model using Gammatone Filter Bank and transfer learning Morshed, Atique Siraj, Farhan Md. Munim, Abu Sadat MD. Ayon, Tasnimul Karim Goni, Saad Rafsan Uddin, Jia Ashraf, Faisal Bin Department of Computer Science and Engineering, Brac University Deep learning Transfer learning Fault detection RNN LSTM Gammatone Filter Bank Artificial intelligence Cognitive learning theory This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 29-30). Rapid industrial growth has increased the vulnerability of systems to malfunction and permanent damage. Fault detection systems have been installed to prevent such occurrences. In order to eliminate potential life-threatening dangers or unforeseen obstacles that may jeopardize the manufacturing process, early fault detection has become an essential aspect of modern industry. Because artificial intelligence has become increasingly successful across numerous different domains, many researchers have employed deep learning models to classify faults and are always trying to find faster, more accurate ones. In this paper, we present a deep transfer learning architecture that consists of long short-term memory (LSTM) layers of Recurrent Neural Network to extract features enhanced by gammatone like spectrogram. For the dataset, we have used malfunctioning industrial machine investigation and inspection (MIMII) and ToyADMOS datasets. Our experimented results show that the proposed model detect the different faults with precision. Also, our modified gammatone fast fourier method outperforms traditional gammatone accurate method with consistent performance across all environments. Atique Morshed Farhan Md. Siraj Abu Sadat MD. Munim Tasnimul Karim Ayon Saad Rafsan Goni B. Computer Science 2021-12-26T05:33:12Z 2021-12-26T05:33:12Z 2021 2021-09 Thesis ID 21141067 ID 17101155 ID 21341046 ID 21141075 ID 17301003 http://hdl.handle.net/10361/15759 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. 30 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Deep learning Transfer learning Fault detection RNN LSTM Gammatone Filter Bank Artificial intelligence Cognitive learning theory
spellingShingle	Deep learning Transfer learning Fault detection RNN LSTM Gammatone Filter Bank Artificial intelligence Cognitive learning theory Morshed, Atique Siraj, Farhan Md. Munim, Abu Sadat MD. Ayon, Tasnimul Karim Goni, Saad Rafsan A texture based industrial fault diagnosis model using Gammatone Filter Bank and transfer learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Uddin, Jia
author_facet	Uddin, Jia Morshed, Atique Siraj, Farhan Md. Munim, Abu Sadat MD. Ayon, Tasnimul Karim Goni, Saad Rafsan
format	Thesis
author	Morshed, Atique Siraj, Farhan Md. Munim, Abu Sadat MD. Ayon, Tasnimul Karim Goni, Saad Rafsan
author_sort	Morshed, Atique
title	A texture based industrial fault diagnosis model using Gammatone Filter Bank and transfer learning
title_short	A texture based industrial fault diagnosis model using Gammatone Filter Bank and transfer learning
title_full	A texture based industrial fault diagnosis model using Gammatone Filter Bank and transfer learning
title_fullStr	A texture based industrial fault diagnosis model using Gammatone Filter Bank and transfer learning
title_full_unstemmed	A texture based industrial fault diagnosis model using Gammatone Filter Bank and transfer learning
title_sort	texture based industrial fault diagnosis model using gammatone filter bank and transfer learning
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15759
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_version_	1814307350391029760

A texture based industrial fault diagnosis model using Gammatone Filter Bank and transfer learning

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