On-line incipient fault detection in single-phase squirrel cage using artificial intelligence / Alvin Bryan Hee Choon Loong
Material type:
TextPublication details: Kuantan, Pahang : UMP, 2009Description: xv, 83 p. : ill. (some col.) ; 30 cm. + 1 computer discISBN: - THE0006821(Local)
- On-line incipient fault detection in single-phase squirrel cage using artificial intelligence [computer file]
| Item type | Current library | Call number | Copy number | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|---|
Final Year Report
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UMPLIB PEKAN | TK4058 .H44 2009 rs Bc. (Browse shelf(Opens below)) | 1 | Not for loan | 0000058380 | ||
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Final Year Report
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UMPLIB PEKAN | CD 5371 | TK4058 .H44 2009 rs Bc. (Browse shelf(Opens below)) | 1 | Not for loan | 0000058381 |
Project paper (Bachelor of Electrical Engineering (Power System)) -- Universiti Malaysia Pahang - 2009
Bibliography : p. 64-65
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