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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2001
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/7948/ http://irep.iium.edu.my/7948/1/079.pdf |
| Summary: | 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. |
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