Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection
In order to enhance the performance of bearing fault diagnosis and classification, features extraction and features dimensionality reduction have become more important. The original statistical feature set was calculated from single branch reconstruction vibration signals obtained by using maximal o...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/5063527 |
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| _version_ | 1849304296852553728 |
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| author | Fei Dong Xiao Yu Enjie Ding Shoupeng Wu Chunyang Fan Yanqiu Huang |
| author_facet | Fei Dong Xiao Yu Enjie Ding Shoupeng Wu Chunyang Fan Yanqiu Huang |
| author_sort | Fei Dong |
| collection | DOAJ |
| description | In order to enhance the performance of bearing fault diagnosis and classification, features extraction and features dimensionality reduction have become more important. The original statistical feature set was calculated from single branch reconstruction vibration signals obtained by using maximal overlap discrete wavelet packet transform (MODWPT). In order to reduce redundancy information of original statistical feature set, features selection by adjusted rand index and sum of within-class mean deviations (FSASD) was proposed to select fault sensitive features. Furthermore, a modified features dimensionality reduction method, supervised neighborhood preserving embedding with label information (SNPEL), was proposed to realize low-dimensional representations for high-dimensional feature space. Finally, vibration signals collected from two experimental test rigs were employed to evaluate the performance of the proposed procedure. The results show that the effectiveness, adaptability, and superiority of the proposed procedure can serve as an intelligent bearing fault diagnosis system. |
| format | Article |
| id | doaj-art-19d97dc83787467bb178fd0aeca8f98a |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-19d97dc83787467bb178fd0aeca8f98a2025-08-20T03:55:45ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/50635275063527Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features SelectionFei Dong0Xiao Yu1Enjie Ding2Shoupeng Wu3Chunyang Fan4Yanqiu Huang5School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, ChinaInstitute of Electrodynamics and Microelectronics, University of Bremen, 28359 Bremen, GermanyIn order to enhance the performance of bearing fault diagnosis and classification, features extraction and features dimensionality reduction have become more important. The original statistical feature set was calculated from single branch reconstruction vibration signals obtained by using maximal overlap discrete wavelet packet transform (MODWPT). In order to reduce redundancy information of original statistical feature set, features selection by adjusted rand index and sum of within-class mean deviations (FSASD) was proposed to select fault sensitive features. Furthermore, a modified features dimensionality reduction method, supervised neighborhood preserving embedding with label information (SNPEL), was proposed to realize low-dimensional representations for high-dimensional feature space. Finally, vibration signals collected from two experimental test rigs were employed to evaluate the performance of the proposed procedure. The results show that the effectiveness, adaptability, and superiority of the proposed procedure can serve as an intelligent bearing fault diagnosis system.http://dx.doi.org/10.1155/2018/5063527 |
| spellingShingle | Fei Dong Xiao Yu Enjie Ding Shoupeng Wu Chunyang Fan Yanqiu Huang Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection Shock and Vibration |
| title | Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection |
| title_full | Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection |
| title_fullStr | Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection |
| title_full_unstemmed | Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection |
| title_short | Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection |
| title_sort | rolling bearing fault diagnosis using modified neighborhood preserving embedding and maximal overlap discrete wavelet packet transform with sensitive features selection |
| url | http://dx.doi.org/10.1155/2018/5063527 |
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