@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}, }