Machine condition monitoring and fault diagnosis using spectral analysis techniques

There is need to continuously monitor the conditions of complex, expensive and process-critical machinery in order to detect its incipient breakdown as well as to ensure its high performance and operating safety. Depending on the application, several techniques are available for monitoring the co...

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Main Authors:	Salami, Momoh Jimoh Eyiomika, Abdul Muthalif, Asan Gani, Pervez, T.
Format:	Conference or Workshop Item
Language:	English
Published:	2001
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/7948/ http://irep.iium.edu.my/7948/1/079.pdf

id	iium-7948
recordtype	eprints
spelling	iium-79482014-11-22T09:22:12Z http://irep.iium.edu.my/7948/ Machine condition monitoring and fault diagnosis using spectral analysis techniques Salami, Momoh Jimoh Eyiomika Abdul Muthalif, Asan Gani Pervez, T. T Technology (General) There is need to continuously monitor the conditions of complex, expensive and process-critical machinery in order to detect its incipient breakdown as well as to ensure its high performance and operating safety. Depending on the application, several techniques are available for monitoring the condition of a machine. Vibration monitoring of rotating machinery is considered in this paper so as develop a selfdiagnosis tool for monitoring machines’ conditions. To achieve this a vibration fault simulation rig (VFSR) is designed and constructed so as to simulate and analyze some of the most common vibration signals encountered in rotating machinery. Vibration data are collected from the piezoelectric accelerometers placed at locations that provide rigid vibration transmission to them. Both normal and fault signals are analyzed using the singular value decomposition (SVD) algorithm so as to compute the parameters of the auto regressive moving average (ARMA) models. Machine condition monitoring is then based on the AR or ARMA spectra so as to overcome some of the limitations of the fast Fourier transform (FFT) techniques. Furthermore the estimated AR model parameters and the distribution of the singular values can be used in conjunction with the spectral peaks in making comparison between healthy and faulty conditions. Different fault conditions have been successfully simulated and analyzed using the VFSR in this paper. Results of analysis clearly indicate that this method of analysis can be further developed and used for self-diagnosis, predictive maintenance and intelligent-based monitoring. 2001 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/7948/1/079.pdf Salami, Momoh Jimoh Eyiomika and Abdul Muthalif, Asan Gani and Pervez, T. (2001) Machine condition monitoring and fault diagnosis using spectral analysis techniques. In: First International Conference on Mechatronics , 12 - 13th February 2001, Kuala Lumpur.
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Salami, Momoh Jimoh Eyiomika Abdul Muthalif, Asan Gani Pervez, T. Machine condition monitoring and fault diagnosis using spectral analysis techniques
description	There is need to continuously monitor the conditions of complex, expensive and process-critical machinery in order to detect its incipient breakdown as well as to ensure its high performance and operating safety. Depending on the application, several techniques are available for monitoring the condition of a machine. Vibration monitoring of rotating machinery is considered in this paper so as develop a selfdiagnosis tool for monitoring machines’ conditions. To achieve this a vibration fault simulation rig (VFSR) is designed and constructed so as to simulate and analyze some of the most common vibration signals encountered in rotating machinery. Vibration data are collected from the piezoelectric accelerometers placed at locations that provide rigid vibration transmission to them. Both normal and fault signals are analyzed using the singular value decomposition (SVD) algorithm so as to compute the parameters of the auto regressive moving average (ARMA) models. Machine condition monitoring is then based on the AR or ARMA spectra so as to overcome some of the limitations of the fast Fourier transform (FFT) techniques. Furthermore the estimated AR model parameters and the distribution of the singular values can be used in conjunction with the spectral peaks in making comparison between healthy and faulty conditions. Different fault conditions have been successfully simulated and analyzed using the VFSR in this paper. Results of analysis clearly indicate that this method of analysis can be further developed and used for self-diagnosis, predictive maintenance and intelligent-based monitoring.
format	Conference or Workshop Item
author	Salami, Momoh Jimoh Eyiomika Abdul Muthalif, Asan Gani Pervez, T.
author_facet	Salami, Momoh Jimoh Eyiomika Abdul Muthalif, Asan Gani Pervez, T.
author_sort	Salami, Momoh Jimoh Eyiomika
title	Machine condition monitoring and fault diagnosis using spectral analysis techniques
title_short	Machine condition monitoring and fault diagnosis using spectral analysis techniques
title_full	Machine condition monitoring and fault diagnosis using spectral analysis techniques
title_fullStr	Machine condition monitoring and fault diagnosis using spectral analysis techniques
title_full_unstemmed	Machine condition monitoring and fault diagnosis using spectral analysis techniques
title_sort	machine condition monitoring and fault diagnosis using spectral analysis techniques
publishDate	2001
url	http://irep.iium.edu.my/7948/ http://irep.iium.edu.my/7948/1/079.pdf
first_indexed	2023-09-18T20:17:29Z
last_indexed	2023-09-18T20:17:29Z
_version_	1777407885136887808

Machine condition monitoring and fault diagnosis using spectral analysis techniques

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