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An improved turbo machinery condition monitoring method using multivariate statistical analysis

Industrial practitioners require a well-structured, proactive and precise conditionmonitoring package in order to optimize turbomachinery operation. Typically, conventional condition monitoring uses built-in software to capture faults or degradation processes based on threshold limits recommended by...

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Main Authors:	Jeyabalan, H., Ooi, C. S., Hui, K. H., Lim, M. H., Leong, M. S.
Format:	Article
Published:	IAEME Publication 2017
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/80813/
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id	my.utm.80813
record_format	eprints
spelling	my.utm.808132019-06-27T07:02:18Z http://eprints.utm.my/id/eprint/80813/ An improved turbo machinery condition monitoring method using multivariate statistical analysis Jeyabalan, H. Ooi, C. S. Hui, K. H. Lim, M. H. Leong, M. S. TK Electrical engineering. Electronics Nuclear engineering Industrial practitioners require a well-structured, proactive and precise conditionmonitoring package in order to optimize turbomachinery operation. Typically, conventional condition monitoring uses built-in software to capture faults or degradation processes based on threshold limits recommended by the Original Equipment Manufacturer (OEM). However, because OEM manual concurrent monitoring involves abundant information parameters, it is dependent on human intervention, insensitive to the development of machinery faults and tends to generate error-prone outcomes. This study proposes a simplified and advanced healthmonitoring method for turbomachinery using a multivariate statistical analysis (MSA) technique. By exploiting mathematical relationships between OEM recommended variables, the significance of input parameter is identified based on weighting factor. With a highly-correlated input subset, the revised condition monitoring method delivershigher sensitivity and a more accurate performance in investigating machine assessment mode. IAEME Publication 2017 Article PeerReviewed Jeyabalan, H. and Ooi, C. S. and Hui, K. H. and Lim, M. H. and Leong, M. S. (2017) An improved turbo machinery condition monitoring method using multivariate statistical analysis. International Journal of Mechanical Engineering and Technology, 8 (5). pp. 1147-1159. ISSN 0976-6340
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Jeyabalan, H. Ooi, C. S. Hui, K. H. Lim, M. H. Leong, M. S. An improved turbo machinery condition monitoring method using multivariate statistical analysis
description	Industrial practitioners require a well-structured, proactive and precise conditionmonitoring package in order to optimize turbomachinery operation. Typically, conventional condition monitoring uses built-in software to capture faults or degradation processes based on threshold limits recommended by the Original Equipment Manufacturer (OEM). However, because OEM manual concurrent monitoring involves abundant information parameters, it is dependent on human intervention, insensitive to the development of machinery faults and tends to generate error-prone outcomes. This study proposes a simplified and advanced healthmonitoring method for turbomachinery using a multivariate statistical analysis (MSA) technique. By exploiting mathematical relationships between OEM recommended variables, the significance of input parameter is identified based on weighting factor. With a highly-correlated input subset, the revised condition monitoring method delivershigher sensitivity and a more accurate performance in investigating machine assessment mode.
format	Article
author	Jeyabalan, H. Ooi, C. S. Hui, K. H. Lim, M. H. Leong, M. S.
author_facet	Jeyabalan, H. Ooi, C. S. Hui, K. H. Lim, M. H. Leong, M. S.
author_sort	Jeyabalan, H.
title	An improved turbo machinery condition monitoring method using multivariate statistical analysis
title_short	An improved turbo machinery condition monitoring method using multivariate statistical analysis
title_full	An improved turbo machinery condition monitoring method using multivariate statistical analysis
title_fullStr	An improved turbo machinery condition monitoring method using multivariate statistical analysis
title_full_unstemmed	An improved turbo machinery condition monitoring method using multivariate statistical analysis
title_sort	improved turbo machinery condition monitoring method using multivariate statistical analysis
publisher	IAEME Publication
publishDate	2017
url	http://eprints.utm.my/id/eprint/80813/
_version_	1643658524465561600
score	13.211869

An improved turbo machinery condition monitoring method using multivariate statistical analysis

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