Diagnosis, classification and prognosis of rotating machine using artificial intelligence

The demand for cost efficient, reliable and safe rotating machinery requires accurate fault diagnosis, classification and prognosis systems. Therefore these issues have become of paramount important so that the potential failures of rotating machinery can be managed properly. Various methods have...

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Bibliographic Details
Main Author:	Mahamad, Abd Kadir
Format:	Thesis
Language:	English
Published:	2010
Subjects:	QC Physics QC251-338.5 Heat
Online Access:	http://eprints.uthm.edu.my/3637/1/24p%20ABD%20KADIR%20MAHAMAD.pdf
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id	my-uthm-ep.3637
record_format	uketd_dc
spelling	my-uthm-ep.36372022-02-03T01:56:36Z Diagnosis, classification and prognosis of rotating machine using artificial intelligence 2010-10 Mahamad, Abd Kadir QC Physics QC251-338.5 Heat The demand for cost efficient, reliable and safe rotating machinery requires accurate fault diagnosis, classification and prognosis systems. Therefore these issues have become of paramount important so that the potential failures of rotating machinery can be managed properly. Various methods have been applied to tackle these issues, but the accuracy of those methods is just satisfactory only. This research, therefore propose appropriate methods for fault diagnosis, classification and prognosis systems. For fault diagnosis and classification, the vibration data was obtained from Western Reserved University. The vibration signal was processed through pre-processing stage, features extraction, features selection before the developed diagnosis and classification model were built. For fault prognosis systems, the acoustic emission and vibration signals were used as input signals. Furthermore, ANN was used as prognosis systems of rotating machinery failure. The simulation results for fault diagnosis, classification and prognosis systems show that proposed methods perform very well and accurate. The proposed model can be used as tools for diagnosing rotating machinery failures. 2010-10 Thesis http://eprints.uthm.edu.my/3637/ http://eprints.uthm.edu.my/3637/1/24p%20ABD%20KADIR%20MAHAMAD.pdf text en public phd doctoral Kumamoto University School of Science and Technology
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English
topic	QC Physics QC251-338.5 Heat
spellingShingle	QC Physics QC251-338.5 Heat Mahamad, Abd Kadir Diagnosis, classification and prognosis of rotating machine using artificial intelligence
description	The demand for cost efficient, reliable and safe rotating machinery requires accurate fault diagnosis, classification and prognosis systems. Therefore these issues have become of paramount important so that the potential failures of rotating machinery can be managed properly. Various methods have been applied to tackle these issues, but the accuracy of those methods is just satisfactory only. This research, therefore propose appropriate methods for fault diagnosis, classification and prognosis systems. For fault diagnosis and classification, the vibration data was obtained from Western Reserved University. The vibration signal was processed through pre-processing stage, features extraction, features selection before the developed diagnosis and classification model were built. For fault prognosis systems, the acoustic emission and vibration signals were used as input signals. Furthermore, ANN was used as prognosis systems of rotating machinery failure. The simulation results for fault diagnosis, classification and prognosis systems show that proposed methods perform very well and accurate. The proposed model can be used as tools for diagnosing rotating machinery failures.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mahamad, Abd Kadir
author_facet	Mahamad, Abd Kadir
author_sort	Mahamad, Abd Kadir
title	Diagnosis, classification and prognosis of rotating machine using artificial intelligence
title_short	Diagnosis, classification and prognosis of rotating machine using artificial intelligence
title_full	Diagnosis, classification and prognosis of rotating machine using artificial intelligence
title_fullStr	Diagnosis, classification and prognosis of rotating machine using artificial intelligence
title_full_unstemmed	Diagnosis, classification and prognosis of rotating machine using artificial intelligence
title_sort	diagnosis, classification and prognosis of rotating machine using artificial intelligence
granting_institution	Kumamoto University
granting_department	School of Science and Technology
publishDate	2010
url	http://eprints.uthm.edu.my/3637/1/24p%20ABD%20KADIR%20MAHAMAD.pdf
_version_	1747831037032923136

Diagnosis, classification and prognosis of rotating machine using artificial intelligence

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