Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings
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, t...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/3843192 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832555926147563520 |
|---|---|
| author | Sharif Uddin Md. Rashedul Islam Sheraz Ali Khan Jaeyoung Kim Jong-Myon Kim Seok-Man Sohn Byeong-Keun Choi |
| author_facet | Sharif Uddin Md. Rashedul Islam Sheraz Ali Khan Jaeyoung Kim Jong-Myon Kim Seok-Man Sohn Byeong-Keun Choi |
| author_sort | Sharif Uddin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-75bb876f594b4cb2b1bf28cef25bc5cd |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-75bb876f594b4cb2b1bf28cef25bc5cd2025-02-03T05:46:51ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/38431923843192Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of BearingsSharif Uddin0Md. Rashedul Islam1Sheraz Ali Khan2Jaeyoung Kim3Jong-Myon Kim4Seok-Man Sohn5Byeong-Keun Choi6School of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan, Republic of KoreaDepartment of Computer Science and Engineering, University of Asia Pacific, Dhaka, BangladeshSchool of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan, Republic of KoreaSchool of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan, Republic of KoreaSchool of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan, Republic of KoreaPower Generation Laboratory, KEPCO Research Institute, Jeollanam-do, Republic of KoreaDepartment of Energy Mechanical Engineering, Gyeongsang National University, Gyeongsangnam-do, Republic of KoreaAn 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.http://dx.doi.org/10.1155/2016/3843192 |
| spellingShingle | Sharif Uddin Md. Rashedul Islam Sheraz Ali Khan Jaeyoung Kim Jong-Myon Kim Seok-Man Sohn Byeong-Keun Choi Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings Shock and Vibration |
| title | Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings |
| title_full | Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings |
| title_fullStr | Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings |
| title_full_unstemmed | Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings |
| title_short | Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings |
| title_sort | distance and density similarity based enhanced k nn classifier for improving fault diagnosis performance of bearings |
| url | http://dx.doi.org/10.1155/2016/3843192 |
| work_keys_str_mv | AT sharifuddin distanceanddensitysimilaritybasedenhancedknnclassifierforimprovingfaultdiagnosisperformanceofbearings AT mdrashedulislam distanceanddensitysimilaritybasedenhancedknnclassifierforimprovingfaultdiagnosisperformanceofbearings AT sherazalikhan distanceanddensitysimilaritybasedenhancedknnclassifierforimprovingfaultdiagnosisperformanceofbearings AT jaeyoungkim distanceanddensitysimilaritybasedenhancedknnclassifierforimprovingfaultdiagnosisperformanceofbearings AT jongmyonkim distanceanddensitysimilaritybasedenhancedknnclassifierforimprovingfaultdiagnosisperformanceofbearings AT seokmansohn distanceanddensitysimilaritybasedenhancedknnclassifierforimprovingfaultdiagnosisperformanceofbearings AT byeongkeunchoi distanceanddensitysimilaritybasedenhancedknnclassifierforimprovingfaultdiagnosisperformanceofbearings |