Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings
Joint Authors
Islam, Md. Rashedul
Kim, Jaeyoung
Khan, Sheraz Ali
Uddin, Sharif
Sohn, Seok-Man
Choi, Byeong-Keun
Kim, Jong-Myon
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-23
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
An enhanced k-nearest neighbor (k-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 k-NN deteriorates in case of overlapping samples and outliers and is highly susceptible to the neighborhood size, k.
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 k-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 k-NN classifier, yields better diagnostic performance and is more robust to variations in the neighborhood size, k.
American Psychological Association (APA)
Uddin, Sharif& Islam, Md. Rashedul& Khan, Sheraz Ali& Kim, Jaeyoung& Kim, Jong-Myon& Sohn, Seok-Man…[et al.]. 2016. Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings. Shock and Vibration،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1119062
Modern Language Association (MLA)
Uddin, Sharif…[et al.]. Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings. Shock and Vibration No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1119062
American Medical Association (AMA)
Uddin, Sharif& Islam, Md. Rashedul& Khan, Sheraz Ali& Kim, Jaeyoung& Kim, Jong-Myon& Sohn, Seok-Man…[et al.]. Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1119062
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1119062