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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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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
score 13.222552

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