@article{(Open Science Index):https://publications.waset.org/pdf/12755,
	  title     = {Generator Damage Recognition Based on Artificial Neural Network},
	  author    = {Chang-Hung Hsu and  Chun-Yao Lee and  Guan-Lin Liao and  Yung-Tsan Jou and  Jin-Maun Ho and  Yu-Hua Hsieh and  Yi-Xing Shen},
	  country	= {},
	  institution	= {},
	  abstract     = {This article simulates the wind generator set which has
two fault bearing collar rail destruction and the gear box oil leak fault.
The electric current signal which produced by the generator, We use
Empirical Mode Decomposition (EMD) as well as Fast Fourier
Transform (FFT) obtains the frequency range-s signal figure and
characteristic value. The last step is use a kind of Artificial Neural
Network (ANN) classifies which determination fault signal's type and
reason. The ANN purpose of the automatic identification wind
generator set fault..},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {6},
	  number    = {5},
	  year      = {2012},
	  pages     = {483 - 486},
	  ee        = {https://publications.waset.org/pdf/12755},
	  url   	= {https://publications.waset.org/vol/65},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 65, 2012},