|
| This Article | ||
| ||
| Share | ||
| Bibliographic References | ||
| Add to: | ||
 Digg  Furl  Spurl  Blink  Simpy  Google  Del.icio.us  Y!MyWeb | ||
| Search | ||
| ||
2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application
Condition Monitoring of Equipment Using a Joint RSAR and Fuzzy ART Neural Network Method
December 19-December 20
ISBN: 978-0-7695-3490-9
| ASCII Text | x | ||
| Jihai Gu, Xianfeng Fan, Ruoming An, Ye Tian, "Condition Monitoring of Equipment Using a Joint RSAR and Fuzzy ART Neural Network Method," Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE, vol. 2, pp. 48-52, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008. | |||
| BibTex | x | ||
| @article{ 10.1109/PACIIA.2008.337, author = {Jihai Gu and Xianfeng Fan and Ruoming An and Ye Tian}, title = {Condition Monitoring of Equipment Using a Joint RSAR and Fuzzy ART Neural Network Method}, journal ={Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE}, volume = {2}, year = {2008}, isbn = {978-0-7695-3490-9}, pages = {48-52}, doi = {http://doi.ieeecomputersociety.org/10.1109/PACIIA.2008.337}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE TI - Condition Monitoring of Equipment Using a Joint RSAR and Fuzzy ART Neural Network Method SN - 978-0-7695-3490-9 SP48 EP52 A1 - Jihai Gu, A1 - Xianfeng Fan, A1 - Ruoming An, A1 - Ye Tian, PY - 2008 VL - 2 JA - Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE ER - | |||
Working conditions are monitoring parameters are huge and neural network learing time too long in the condition monitoring of multi word condition equipment. To improve monitoring efficiency, a joint Rough Set Attribute Reduction (RSAR) and Fuzzy ART (Adaptive Resonance Theory) neural network method is proposed in this study. The dimension of an input vector to Fuzzy ART neural networks can be reduced through RSAR. The updated vectors are used to train Fuzzy ART neural networks. An example is investigated to evaluate the proposed method in this study. Analysis results indicate that the proposed method can save great learning time without losing monitoring capability. Additionally, sensor abnormality and signal transmission issues may be detected as well.
Citation:
Jihai Gu, Xianfeng Fan, Ruoming An, Ye Tian, "Condition Monitoring of Equipment Using a Joint RSAR and Fuzzy ART Neural Network Method," paciia, vol. 2, pp.48-52, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008
Usage of this product signifies your acceptance of the Terms of Use.