Rolling Element Bearing Fault Recognition Approach Based on Fuzzy Clustering Bispectrum Estimation
A rolling element bearing fault recognition approach is proposed in this paper. This method combines the basic Higher-order spectrum (HOS) theory and fuzzy clustering method in data mining area. In the first step, all the bispectrum estimation results of the training samples and test samples are tur...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.3233/SAV-2012-00739 |
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| author | W.Y. Liu J.G. Han |
| author_facet | W.Y. Liu J.G. Han |
| author_sort | W.Y. Liu |
| collection | DOAJ |
| description | A rolling element bearing fault recognition approach is proposed in this paper. This method combines the basic Higher-order spectrum (HOS) theory and fuzzy clustering method in data mining area. In the first step, all the bispectrum estimation results of the training samples and test samples are turned into binary feature images. Secondly, the binary feature images of the training samples are used to construct object templates including kernel images and domain images. Every fault category has one object templates. At last, by calculating the distances between test samples' binary feature images and the different object templates, the object classification and pattern recognition can be effectively accomplished. Bearing is the most important and much easier to be damaged component in rotating machinery. Furthermore, there exist large amounts of noise jamming and nonlinear coupling components in bearing vibration signals. The Higher Order Cumulants (HOC), which can quantitatively describe the nonlinear characteristic signals with close relationship between the mechanical faults, is introduced in this paper to de-noise the raw bearing vibration signals and obtain the bispectrum estimation pictures. In the experimental part, the rolling bearing fault diagnosis experiment results proved that the classification was completely correct. |
| format | Article |
| id | doaj-art-d7f7cf5de0ac46799cd6f3d4ba0889c4 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-d7f7cf5de0ac46799cd6f3d4ba0889c42025-02-03T01:26:27ZengWileyShock and Vibration1070-96221875-92032013-01-0120221322510.3233/SAV-2012-00739Rolling Element Bearing Fault Recognition Approach Based on Fuzzy Clustering Bispectrum EstimationW.Y. Liu0J.G. Han1School of Mechanical and Electrical Engineering, Jiangsu Normal University, Xuzhou, Jiangsu, ChinaSchool of Mechanical and Electrical Engineering, Jiangsu Normal University, Xuzhou, Jiangsu, ChinaA rolling element bearing fault recognition approach is proposed in this paper. This method combines the basic Higher-order spectrum (HOS) theory and fuzzy clustering method in data mining area. In the first step, all the bispectrum estimation results of the training samples and test samples are turned into binary feature images. Secondly, the binary feature images of the training samples are used to construct object templates including kernel images and domain images. Every fault category has one object templates. At last, by calculating the distances between test samples' binary feature images and the different object templates, the object classification and pattern recognition can be effectively accomplished. Bearing is the most important and much easier to be damaged component in rotating machinery. Furthermore, there exist large amounts of noise jamming and nonlinear coupling components in bearing vibration signals. The Higher Order Cumulants (HOC), which can quantitatively describe the nonlinear characteristic signals with close relationship between the mechanical faults, is introduced in this paper to de-noise the raw bearing vibration signals and obtain the bispectrum estimation pictures. In the experimental part, the rolling bearing fault diagnosis experiment results proved that the classification was completely correct.http://dx.doi.org/10.3233/SAV-2012-00739 |
| spellingShingle | W.Y. Liu J.G. Han Rolling Element Bearing Fault Recognition Approach Based on Fuzzy Clustering Bispectrum Estimation Shock and Vibration |
| title | Rolling Element Bearing Fault Recognition Approach Based on Fuzzy Clustering Bispectrum Estimation |
| title_full | Rolling Element Bearing Fault Recognition Approach Based on Fuzzy Clustering Bispectrum Estimation |
| title_fullStr | Rolling Element Bearing Fault Recognition Approach Based on Fuzzy Clustering Bispectrum Estimation |
| title_full_unstemmed | Rolling Element Bearing Fault Recognition Approach Based on Fuzzy Clustering Bispectrum Estimation |
| title_short | Rolling Element Bearing Fault Recognition Approach Based on Fuzzy Clustering Bispectrum Estimation |
| title_sort | rolling element bearing fault recognition approach based on fuzzy clustering bispectrum estimation |
| url | http://dx.doi.org/10.3233/SAV-2012-00739 |
| work_keys_str_mv | AT wyliu rollingelementbearingfaultrecognitionapproachbasedonfuzzyclusteringbispectrumestimation AT jghan rollingelementbearingfaultrecognitionapproachbasedonfuzzyclusteringbispectrumestimation |