A descriptive study on development of a transfer learning based fault detection model using 2D CNN for air compressors

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

Bibliografiske detaljer
Main Authors:	Hasan, Moenul, Uddin, A.K.M. Faiyaz, Ghosh, Anoup
Andre forfattere:	Uddin, Jia
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2021
Fag:	Fault Detection Machine Learning Deep Learning Real-Time Fault Classification Wavelet Transformation 2D Image DCNN Keras Fault location (Engineering)
Online adgang:	http://hdl.handle.net/10361/14985

id	10361-14985
record_format	dspace
spelling	10361-149852022-01-26T10:13:21Z A descriptive study on development of a transfer learning based fault detection model using 2D CNN for air compressors Hasan, Moenul Uddin, A.K.M. Faiyaz Ghosh, Anoup Uddin, Jia Khan, Rubayat Ahmed Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Fault Detection Machine Learning Deep Learning Real-Time Fault Classification Wavelet Transformation 2D Image DCNN Keras Fault location (Engineering) 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 51-54). Fault Detection is essential for the safe and efficient operation of industrial manufacturing. Successful detection of fault features allows us to maintain a stableproduc- tion line. Therefore, establishing a reliable and accurate fault detection method has become a huge priority now. Historically, various artificial intelligence-based models are used to predict faults in machines accurately to some extent. Mainly, machine learning and deep learning-based processes are being used. However, there are some shortcomings in those processes. Firstly, machine learning is mostly dependent on previous data and fails to recognize new issues that have not been introduced to the model during the training phase. Secondly, with deep learning, it is very time- consuming to reliably classify faults and difficult to establish an effective model for complex systems of current days. Thus, in our paper, we are proposing to use a transfer learning-based optimization of the deep learning process to meet the re- quirements ofreal-time fault classification and accurate detection of faultsin adverse operational conditions. We will be using wavelet transformation of raw signal data to 2D images and constructing a DCNN based transfer learning architecture toex- tract the fault features of the machine. Finally, we will be feeding the network with data from our target domain for fine-tuning the network to work accurately in the target domain. We will be testing with two cases to find the accuracy and accuracy optimization over the deep learning (AAG) values of our system. Finally, we will be comparing our architecture with state-of-the-art transfer learning architectures from Keras. Moenul Hasan A.K.M. Faiyaz Uddin Anoup Ghosh B. Computer Science 2021-09-07T13:48:31Z 2021-09-07T13:48:31Z 2021 2021-06 Thesis ID 17301119 ID 17301088 ID 17101518 http://hdl.handle.net/10361/14985 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. 54 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Fault Detection Machine Learning Deep Learning Real-Time Fault Classification Wavelet Transformation 2D Image DCNN Keras Fault location (Engineering)
spellingShingle	Fault Detection Machine Learning Deep Learning Real-Time Fault Classification Wavelet Transformation 2D Image DCNN Keras Fault location (Engineering) Hasan, Moenul Uddin, A.K.M. Faiyaz Ghosh, Anoup A descriptive study on development of a transfer learning based fault detection model using 2D CNN for air compressors
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 Hasan, Moenul Uddin, A.K.M. Faiyaz Ghosh, Anoup
format	Thesis
author	Hasan, Moenul Uddin, A.K.M. Faiyaz Ghosh, Anoup
author_sort	Hasan, Moenul
title	A descriptive study on development of a transfer learning based fault detection model using 2D CNN for air compressors
title_short	A descriptive study on development of a transfer learning based fault detection model using 2D CNN for air compressors
title_full	A descriptive study on development of a transfer learning based fault detection model using 2D CNN for air compressors
title_fullStr	A descriptive study on development of a transfer learning based fault detection model using 2D CNN for air compressors
title_full_unstemmed	A descriptive study on development of a transfer learning based fault detection model using 2D CNN for air compressors
title_sort	descriptive study on development of a transfer learning based fault detection model using 2d cnn for air compressors
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/14985
work_keys_str_mv	AT hasanmoenul adescriptivestudyondevelopmentofatransferlearningbasedfaultdetectionmodelusing2dcnnforaircompressors AT uddinakmfaiyaz adescriptivestudyondevelopmentofatransferlearningbasedfaultdetectionmodelusing2dcnnforaircompressors AT ghoshanoup adescriptivestudyondevelopmentofatransferlearningbasedfaultdetectionmodelusing2dcnnforaircompressors AT hasanmoenul descriptivestudyondevelopmentofatransferlearningbasedfaultdetectionmodelusing2dcnnforaircompressors AT uddinakmfaiyaz descriptivestudyondevelopmentofatransferlearningbasedfaultdetectionmodelusing2dcnnforaircompressors AT ghoshanoup descriptivestudyondevelopmentofatransferlearningbasedfaultdetectionmodelusing2dcnnforaircompressors
_version_	1814308140893601792

A descriptive study on development of a transfer learning based fault detection model using 2D CNN for air compressors

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