Hee, Alvin Bryan Choon Loong (2009) On-line incipient fault detection in single-phase squirrel cage using artificial intelligence. Faculty Of Electrical & Electronic Engineering, Universiti Malaysia Pahang.
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Abstract
This project creates and develops an artificial neural network that is capable to determine the condition of a motor whether it is in a healthy state or fault state. All of the data used to train the artificial neural network is obtained by using the result from the simulation of MATLAB Simulink model that represent the real motor. The artificial neural network is trained by using radial basis function neural network method. MATLAB is used to construct and develop Graphical User Interface and interface it with the artificial neural network created. By doing so, the user will be able to test the neural network created with ease of using the Graphical User Interface
| Item Type: | Undergraduates Project Papers |
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| Additional Information: | Project paper (Bachelor of Electrical Engineering (Power System)) -- Universiti Malaysia Pahang - 2009, SV: DR AHMED N ABDUL ALLA, NO. CD: 5371 |
| Uncontrolled Keywords: | Electric driving; Automatic control |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical & Electronic Engineering |
| Depositing User: | Syed Mohd Faiz |
| Date Deposited: | 10 Apr 2012 06:06 |
| Last Modified: | 18 Oct 2023 07:32 |
| URI: | http://umpir.ump.edu.my/id/eprint/1930 |
| Download Statistic: | View Download Statistics |
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