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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| 格式: | Thesis |
| 語言: | English English English |
| 出版: |
2005
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| 主題: | |
| 在線閱讀: | 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 |
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| 總結: | 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 |
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