Diagnosis, classification and prognosis of rotating machine using artificial intelligence
The demand for cost efficient, reliable and safe rotating machinery requires accurate fault diagnosis, classification and prognosis systems. Therefore these issues have become of paramount important so that the potential failures of rotating machinery can be managed properly. Various methods have...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/3637/1/24p%20ABD%20KADIR%20MAHAMAD.pdf |
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| Summary: | The demand for cost efficient, reliable and safe rotating machinery requires accurate
fault diagnosis, classification and prognosis systems. Therefore these issues
have become of paramount important so that the potential failures of rotating
machinery can be managed properly. Various methods have been applied to tackle
these issues, but the accuracy of those methods is just satisfactory only. This
research, therefore propose appropriate methods for fault diagnosis, classification
and prognosis systems. For fault diagnosis and classification, the vibration data
was obtained from Western Reserved University. The vibration signal was processed
through pre-processing stage, features extraction, features selection before
the developed diagnosis and classification model were built. For fault prognosis
systems, the acoustic emission and vibration signals were used as input signals.
Furthermore, ANN was used as prognosis systems of rotating machinery failure.
The simulation results for fault diagnosis, classification and prognosis systems
show that proposed methods perform very well and accurate. The proposed
model can be used as tools for diagnosing rotating machinery failures. |
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