Monitoring and prediction of bearing failure by acoustic emission and neural network
The purpose of this research is to develop an appropriate ANN model of bearing failure prediction. Acoustic emission (AE) represented the technique of collecting the data that was collected from the bearing and this data were measured in term of decibel (dB) and Distress level. The data was th...
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| Format: | Thesis |
| Language: | en en en |
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2005
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| Online Access: | http://eprints.uthm.edu.my/7938/1/24p%20ABD%20KADIR%20MAHAMAD.pdf http://eprints.uthm.edu.my/7938/2/ABD%20KADIR%20MAHAMAD%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/7938/3/ABD%20KADIR%20MAHAMAD%20WATERMARK.pdf http://eprints.uthm.edu.my/7938/ |
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| _version_ | 1833418523701936128 |
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| 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 purpose of this research is to develop an appropriate ANN model of
bearing failure prediction. Acoustic emission (AE) represented the technique of
collecting the data that was collected from the bearing and this data were measured
in term of decibel (dB) and Distress level. The data was then used to develop thc
model using ANN for bearing fault prediction model. An experimental rig was setup
to collect data on bearing by using Machine Health Checker (MI-IC) Memo assist
with MHC Analysis software. In the development of ANN modeling, the result
obtained shows that the optimum model was Elman network with training algorithm.
Levenberg-Marquardt Back-propagation and the suitable transfer function for hidden
node and output node was logsig/purelin combination. Four models were built in
this research for multiple step ahead prediction, that were one day ahead model
(Modell), seven days ahead model (Model 2), fourteen days ahead (Model 3) and
thirty days ahead model (Model 4). In the application part, a computer program was
written on bearing failure prediction. This program was implementcd using graphical
user interface (OUI) features that can be implemented by using a MA TLAB OUr. In
the end, the user was able to use this program as a tool to operate or simulate bcaring
failure prediction |
| format | Thesis |
| id | my.uthm.eprints-7938 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en en en |
| publishDate | 2005 |
| record_format | eprints |
| spelling | my.uthm.eprints-79382022-10-30T08:05:38Z http://eprints.uthm.edu.my/7938/ Monitoring and prediction of bearing failure by acoustic emission and neural network Mahamad, Abd Kadir TJ Mechanical engineering and machinery TJ1040-1119 Machinery exclusive of prime movers The purpose of this research is to develop an appropriate ANN model of bearing failure prediction. Acoustic emission (AE) represented the technique of collecting the data that was collected from the bearing and this data were measured in term of decibel (dB) and Distress level. The data was then used to develop thc model using ANN for bearing fault prediction model. An experimental rig was setup to collect data on bearing by using Machine Health Checker (MI-IC) Memo assist with MHC Analysis software. In the development of ANN modeling, the result obtained shows that the optimum model was Elman network with training algorithm. Levenberg-Marquardt Back-propagation and the suitable transfer function for hidden node and output node was logsig/purelin combination. Four models were built in this research for multiple step ahead prediction, that were one day ahead model (Modell), seven days ahead model (Model 2), fourteen days ahead (Model 3) and thirty days ahead model (Model 4). In the application part, a computer program was written on bearing failure prediction. This program was implementcd using graphical user interface (OUI) features that can be implemented by using a MA TLAB OUr. In the end, the user was able to use this program as a tool to operate or simulate bcaring failure prediction 2005-03 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/7938/1/24p%20ABD%20KADIR%20MAHAMAD.pdf text en http://eprints.uthm.edu.my/7938/2/ABD%20KADIR%20MAHAMAD%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/7938/3/ABD%20KADIR%20MAHAMAD%20WATERMARK.pdf Mahamad, Abd Kadir (2005) Monitoring and prediction of bearing failure by acoustic emission and neural network. Masters thesis, Kolej Universiti Teknologi Tun Hussein Onn. |
| spellingShingle | TJ Mechanical engineering and machinery TJ1040-1119 Machinery exclusive of prime movers Mahamad, Abd Kadir Monitoring and prediction of bearing failure by acoustic emission and neural network |
| title | Monitoring and prediction of bearing failure by acoustic emission and neural network |
| title_full | Monitoring and prediction of bearing failure by acoustic emission and neural network |
| title_fullStr | Monitoring and prediction of bearing failure by acoustic emission and neural network |
| title_full_unstemmed | Monitoring and prediction of bearing failure by acoustic emission and neural network |
| title_short | Monitoring and prediction of bearing failure by acoustic emission and neural network |
| title_sort | monitoring and prediction of bearing failure by acoustic emission and neural network |
| topic | TJ Mechanical engineering and machinery TJ1040-1119 Machinery exclusive of prime movers |
| url | http://eprints.uthm.edu.my/7938/1/24p%20ABD%20KADIR%20MAHAMAD.pdf http://eprints.uthm.edu.my/7938/2/ABD%20KADIR%20MAHAMAD%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/7938/3/ABD%20KADIR%20MAHAMAD%20WATERMARK.pdf http://eprints.uthm.edu.my/7938/ |
| url_provider | http://eprints.uthm.edu.my/ |