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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.

Abstract

An enhanced -nearest neighbor (-NN) classification algorithm is presented, which uses a density based similarity measure in addition to a distance based similarity measure to improve the diagnostic performance in bearing fault diagnosis. Due to its use of distance based similarity measure alone, the classification accuracy of traditional -NN deteriorates in case of overlapping samples and outliers and is highly susceptible to the neighborhood size, . This study addresses these limitations by proposing the use of both distance and density based measures of similarity between training and test samples. The proposed -NN classifier is used to enhance the diagnostic performance of a bearing fault diagnosis scheme, which classifies different fault conditions based upon hybrid feature vectors extracted from acoustic emission (AE) signals. Experimental results demonstrate that the proposed scheme, which uses the enhanced -NN classifier, yields better diagnostic performance and is more robust to variations in the neighborhood size, .