Health status estimation of transceiver testing machine using adaptive neuro fuzzy inference system

The purpose of this study is to improve existing maintenance triggering process for Transceiver Testing Machine (TTM) in Finisar by identifying the influence factors and design a new Adaptive Neuro Fuzzy Inference System (ANFIS) model as a machine learning model approach. The ANFIS model have a capa...

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Main Author:	Najmi, Muhammad Amzari
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/98388/1/MuhammadAmzariNajmiMSC2018.pdf.pdf
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id	my-utm-ep.98388
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spelling	my-utm-ep.983882022-12-12T01:08:13Z Health status estimation of transceiver testing machine using adaptive neuro fuzzy inference system 2018 Najmi, Muhammad Amzari QA Mathematics The purpose of this study is to improve existing maintenance triggering process for Transceiver Testing Machine (TTM) in Finisar by identifying the influence factors and design a new Adaptive Neuro Fuzzy Inference System (ANFIS) model as a machine learning model approach. The ANFIS model have a capability to learn the behavior of its production historical data and estimate the health status. This health status will be used as a trigger alarm for maintenance team to perform maintenance task instead of just using default expiry time setting as a reference. The ANFIS model were validated using production historical data from several scenarios as case studies. The accuracy of training and testing data were analyzed in this study. It is shown that the ANFIS model developed give good performance and results of this study can be used as adequate reference to improve the maintenance triggering process in industry such as Finisar. 2018 Thesis http://eprints.utm.my/id/eprint/98388/ http://eprints.utm.my/id/eprint/98388/1/MuhammadAmzariNajmiMSC2018.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:144599 masters Universiti Teknologi Malaysia Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Najmi, Muhammad Amzari Health status estimation of transceiver testing machine using adaptive neuro fuzzy inference system
description	The purpose of this study is to improve existing maintenance triggering process for Transceiver Testing Machine (TTM) in Finisar by identifying the influence factors and design a new Adaptive Neuro Fuzzy Inference System (ANFIS) model as a machine learning model approach. The ANFIS model have a capability to learn the behavior of its production historical data and estimate the health status. This health status will be used as a trigger alarm for maintenance team to perform maintenance task instead of just using default expiry time setting as a reference. The ANFIS model were validated using production historical data from several scenarios as case studies. The accuracy of training and testing data were analyzed in this study. It is shown that the ANFIS model developed give good performance and results of this study can be used as adequate reference to improve the maintenance triggering process in industry such as Finisar.
format	Thesis
qualification_level	Master's degree
author	Najmi, Muhammad Amzari
author_facet	Najmi, Muhammad Amzari
author_sort	Najmi, Muhammad Amzari
title	Health status estimation of transceiver testing machine using adaptive neuro fuzzy inference system
title_short	Health status estimation of transceiver testing machine using adaptive neuro fuzzy inference system
title_full	Health status estimation of transceiver testing machine using adaptive neuro fuzzy inference system
title_fullStr	Health status estimation of transceiver testing machine using adaptive neuro fuzzy inference system
title_full_unstemmed	Health status estimation of transceiver testing machine using adaptive neuro fuzzy inference system
title_sort	health status estimation of transceiver testing machine using adaptive neuro fuzzy inference system
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Computing
publishDate	2018
url	http://eprints.utm.my/id/eprint/98388/1/MuhammadAmzariNajmiMSC2018.pdf.pdf
_version_	1776100584613478400

Health status estimation of transceiver testing machine using adaptive neuro fuzzy inference system

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