Wind turbine blades fault detection based on principal component analysis

International Conference on Applications and Design in Mechanical Engineering 2012 (ICADME 2012) organized by School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 27th - 28th Februari 2012 at Bayview Beach Resort, Penang, Malaysia.

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Bibliographic Details
Main Authors:	Abdelnasser, Abouhnik, Ghalib R., Ibrahim, Mohammed sh-eldin, A. Albarbar
Other Authors:	abouhnik@yahoo.com
Format:	Working Paper
Language:	English
Published:	Universiti Malaysia Perlis (UniMAP) 2012
Subjects:	Principal Components Analysis (PCA) Crack Residual Matrix
Online Access:	http://dspace.unimap.edu.my/xmlui/handle/123456789/20237
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Institution:	Universiti Malaysia Perlis
Language:	English

id	my.unimap-20237
record_format	dspace
spelling	my.unimap-202372012-07-10T05:12:57Z Wind turbine blades fault detection based on principal component analysis Abdelnasser, Abouhnik Ghalib R., Ibrahim Mohammed sh-eldin A. Albarbar abouhnik@yahoo.com ghalib_ibrahim@hotmail.com a.albarbar@mmu.ac.uk Principal Components Analysis (PCA) Crack Residual Matrix International Conference on Applications and Design in Mechanical Engineering 2012 (ICADME 2012) organized by School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 27th - 28th Februari 2012 at Bayview Beach Resort, Penang, Malaysia. This paper presents a new approach to detect faults in wind turbine blades. This approach is based on Principal Component Analysis (PCA) of the vibration signal. The residual matrix signals for healthy and faulty system were compared by applying the crest factor. It contains information extracted from the PCA and the faults were found from the comparisons. The experimental work was carried out using three bladed wind turbine. The cracks were simulated on the blade with diameters (3 mm, 6 mm, 9 mm and 12 mm), all had a consistent depth 3 mm. The tests were carried out for two rotation speeds; 250 and 360 rpm. The results showed that PCA of vibration based condition monitoring is a promising technique because it contains information on all the components of the wind turbine contained in the vibration signal. The crest factor was calculated for the PCA residual matrix. The novel approach successfully differentiated the signals from healthy system and system containing cracks in a turbine blade. 2012-07-10T05:12:57Z 2012-07-10T05:12:57Z 2012-02-27 Working Paper http://hdl.handle.net/123456789/20237 en Proceedings of the International Conference on Applications and Design in Mechanical Engineering 2012 (ICADME 2012) Universiti Malaysia Perlis (UniMAP) Pusat Pengajian Kejuruteraan Mekatronik
institution	Universiti Malaysia Perlis
building	UniMAP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Perlis
content_source	UniMAP Library Digital Repository
url_provider	http://dspace.unimap.edu.my/
language	English
topic	Principal Components Analysis (PCA) Crack Residual Matrix
spellingShingle	Principal Components Analysis (PCA) Crack Residual Matrix Abdelnasser, Abouhnik Ghalib R., Ibrahim Mohammed sh-eldin A. Albarbar Wind turbine blades fault detection based on principal component analysis
description	International Conference on Applications and Design in Mechanical Engineering 2012 (ICADME 2012) organized by School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 27th - 28th Februari 2012 at Bayview Beach Resort, Penang, Malaysia.
author2	abouhnik@yahoo.com
author_facet	abouhnik@yahoo.com Abdelnasser, Abouhnik Ghalib R., Ibrahim Mohammed sh-eldin A. Albarbar
format	Working Paper
author	Abdelnasser, Abouhnik Ghalib R., Ibrahim Mohammed sh-eldin A. Albarbar
author_sort	Abdelnasser, Abouhnik
title	Wind turbine blades fault detection based on principal component analysis
title_short	Wind turbine blades fault detection based on principal component analysis
title_full	Wind turbine blades fault detection based on principal component analysis
title_fullStr	Wind turbine blades fault detection based on principal component analysis
title_full_unstemmed	Wind turbine blades fault detection based on principal component analysis
title_sort	wind turbine blades fault detection based on principal component analysis
publisher	Universiti Malaysia Perlis (UniMAP)
publishDate	2012
url	http://dspace.unimap.edu.my/xmlui/handle/123456789/20237
_version_	1643793017731022848

Wind turbine blades fault detection based on principal component analysis

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