000 01869nam a2200277 a 4500
001 vtls000055171
003 KUKTEM
005 20251114204338.0
008 110802t2009 my a f m 000 0 eng d
020 _aTHE0006821(Local)
039 9 _a201905291242
_bamirul
_y201108020925
_zFida
040 _aUMP
090 _aTK4058 .H44 2009 rs Bc.
100 1 _aHee, Alvin Bryan Choon Loong
245 1 0 _aOn-line incipient fault detection in single-phase squirrel cage using artificial intelligence /
_cAlvin Bryan Hee Choon Loong
246 3 _aOn-line incipient fault detection in single-phase squirrel cage using artificial intelligence
_h[computer file]
260 _aKuantan, Pahang :
_bUMP,
_c2009
300 _axv, 83 p. :
_bill. (some col.) ;
_c30 cm. +
_e1 computer disc
502 _aProject paper (Bachelor of Electrical Engineering (Power System)) -- Universiti Malaysia Pahang - 2009
504 _aBibliography : p. 64-65
520 3 _aThis 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
650 0 _aElectric driving
_xAutomatic control
650 0 _aElectric motors
_xAutomatic control
650 0 _aArtificial intelligence
999 _aVIRTUA40
_c417
_d423
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5040*5200*6500*6501*6502*9992