Name
Title | ||
|---|---|---|
Reliable Fault Diagnosis Of Bearings Using Distance And Density Similarity On An Enhanced K-Nn |
Abstract | ||
|---|---|---|
The k-nearest neighbor (k-NN) method is a simple and highly effective classifier, but the classification accuracy of k-NN is degraded and becomes highly sensitive to the neighborhood size k in multi-classification problems, where the density of data samples varies across different classes. This is mainly due to the method using only a distance-based measure of similarity between different samples. In this paper, we propose a density-weighted distance similarity metric, which considers the relative densities of samples in addition to the distances between samples to improve the classification accuracy of standard k-NN. The performance of the proposed k-NN approach is not affected by the neighborhood size k. Experimental results show that the proposed approach yields better classification accuracy than traditional k-NN for fault diagnosis of rolling element bearings. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-51691-2_17 | ARTIFICIAL LIFE AND COMPUTATIONAL INTELLIGENCE, ACALCI 2017 |
Keywords | DocType | Volume |
K-NN, Fault diagnosis, Bearings, Distance-based similarity, Density-based similarity | Conference | 10142 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dileep Kumar Appana | 1 | 8 | 1.27 |
| Md. Rashedul Islam | 2 | 0 | 0.68 |
| Jong Myon Kim | 3 | 144 | 32.36 |