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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | en |
| Published: |
2010
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/3637/1/24p%20ABD%20KADIR%20MAHAMAD.pdf http://eprints.uthm.edu.my/3637/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833417389798064128 |
|---|---|
| author | Mahamad, Abd Kadir |
| author_facet | Mahamad, Abd Kadir |
| author_sort | Mahamad, Abd Kadir |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Thesis |
| id | my.uthm.eprints-3637 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2010 |
| record_format | eprints |
| spelling | my.uthm.eprints-36372022-02-03T01:56:36Z http://eprints.uthm.edu.my/3637/ Diagnosis, classification and prognosis of rotating machine using artificial intelligence Mahamad, Abd Kadir QC Physics QC251-338.5 Heat 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. 2010-10 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/3637/1/24p%20ABD%20KADIR%20MAHAMAD.pdf Mahamad, Abd Kadir (2010) Diagnosis, classification and prognosis of rotating machine using artificial intelligence. Doctoral thesis, Kumamoto University. |
| spellingShingle | QC Physics QC251-338.5 Heat Mahamad, Abd Kadir Diagnosis, classification and prognosis of rotating machine using artificial intelligence |
| title | Diagnosis, classification and prognosis of rotating machine using artificial intelligence |
| title_full | Diagnosis, classification and prognosis of rotating machine using artificial intelligence |
| title_fullStr | Diagnosis, classification and prognosis of rotating machine using artificial intelligence |
| title_full_unstemmed | Diagnosis, classification and prognosis of rotating machine using artificial intelligence |
| title_short | Diagnosis, classification and prognosis of rotating machine using artificial intelligence |
| title_sort | diagnosis, classification and prognosis of rotating machine using artificial intelligence |
| topic | QC Physics QC251-338.5 Heat |
| url | http://eprints.uthm.edu.my/3637/1/24p%20ABD%20KADIR%20MAHAMAD.pdf http://eprints.uthm.edu.my/3637/ |
| url_provider | http://eprints.uthm.edu.my/ |