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Shock and Vibration
Volume 2016 (2016), Article ID 3843192, 11 pages
http://dx.doi.org/10.1155/2016/3843192
Research Article

Distance and Density Similarity Based Enhanced -NN Classifier for Improving Fault Diagnosis Performance of Bearings

1School of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan, Republic of Korea
2Department of Computer Science and Engineering, University of Asia Pacific, Dhaka, Bangladesh
3Power Generation Laboratory, KEPCO Research Institute, Jeollanam-do, Republic of Korea
4Department of Energy Mechanical Engineering, Gyeongsang National University, Gyeongsangnam-do, Republic of Korea

Received 1 September 2016; Accepted 17 October 2016

Academic Editor: Lu Chen

Copyright © 2016 Sharif Uddin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to Cite this Article

Sharif Uddin, Md. Rashedul Islam, Sheraz Ali Khan, et al., “Distance and Density Similarity Based Enhanced -NN Classifier for Improving Fault Diagnosis Performance of Bearings,” Shock and Vibration, vol. 2016, Article ID 3843192, 11 pages, 2016. doi:10.1155/2016/3843192