Machine condition monitoring and fault diagnosis using spectral analysis techniques

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.

Preview

PDF (Machine condition monitoring and fault diagnosis using spectral analysis techniques) - Published Version Download (252kB) | Preview

Abstract

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.

Item Type:	Conference or Workshop Item (Full Paper)
Additional Information:	3352/7948
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Prof Momoh-Jimoh Salami
Date Deposited:	13 May 2013 11:48
Last Modified:	22 Nov 2014 17:22
URI:	http://irep.iium.edu.my/id/eprint/7948

Actions (login required)

View Item