The Application of Orthogonal Wavelet Transformation: Support Vector Data Description in Evaluating the Performance and Health of Bearings
Support vector data description (SVDD) is common supervised learning. Its basic idea is to establish a closed and compact area with the objects to be described as integrity. The described objects are all included within the area or as far as possible. In contrast, other objects are excluded out of t...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/2741616 |
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| author | Weipeng Li Yan Cao Lijuan Li Siyu Hou |
| author_facet | Weipeng Li Yan Cao Lijuan Li Siyu Hou |
| author_sort | Weipeng Li |
| collection | DOAJ |
| description | Support vector data description (SVDD) is common supervised learning. Its basic idea is to establish a closed and compact area with the objects to be described as integrity. The described objects are all included within the area or as far as possible. In contrast, other objects are excluded out of the area as far as possible. The inherent nature and laws of data are subsequently revealed, thereby distinguishing the operation state of the machine. In this paper, an orthogonal wavelet transformation-support vector data description (OWTSVDD) is proposed to evaluate the performance of bearings, where the peak-to-peak value of detail signal is extracted through orthogonal wavelet transformation as the set of test samples, thus solving the distance Rz from the set of test samples to the center of the sphere. Based on HI=Rz2−R2, its distance to the hypersphere is calculated to judge whether it belongs to the normal state training samples. Finally, the performance and health of bearings are evaluated with HI. According to the classification of two sets of experimental data of rolling bearings, the proposed method better reflects the degeneration of bearing’s performance compared with the (SVDD) HI value without extraction of characteristic value, being entirely able to evaluate the entire life cycle of bearings from normal operation to fault and degradation. The HI evaluation result based on experimental data in Xi’an Jiaotong University is consistent with the life-cycle vibration signal of bearings, providing a scientific basis for production and equipment management and improving the prognostics technology-centered prognostics and health management (PHM). |
| format | Article |
| id | doaj-art-d5209d12082d4391a8d925ee21f7f54f |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-d5209d12082d4391a8d925ee21f7f54f2025-02-03T06:01:51ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/2741616The Application of Orthogonal Wavelet Transformation: Support Vector Data Description in Evaluating the Performance and Health of BearingsWeipeng Li0Yan Cao1Lijuan Li2Siyu Hou3School of Mechanical Electrical EngineeringSchool of Mechanical Electrical EngineeringSchool of Mechanical Electrical EngineeringSchool of Mechanical Electrical EngineeringSupport vector data description (SVDD) is common supervised learning. Its basic idea is to establish a closed and compact area with the objects to be described as integrity. The described objects are all included within the area or as far as possible. In contrast, other objects are excluded out of the area as far as possible. The inherent nature and laws of data are subsequently revealed, thereby distinguishing the operation state of the machine. In this paper, an orthogonal wavelet transformation-support vector data description (OWTSVDD) is proposed to evaluate the performance of bearings, where the peak-to-peak value of detail signal is extracted through orthogonal wavelet transformation as the set of test samples, thus solving the distance Rz from the set of test samples to the center of the sphere. Based on HI=Rz2−R2, its distance to the hypersphere is calculated to judge whether it belongs to the normal state training samples. Finally, the performance and health of bearings are evaluated with HI. According to the classification of two sets of experimental data of rolling bearings, the proposed method better reflects the degeneration of bearing’s performance compared with the (SVDD) HI value without extraction of characteristic value, being entirely able to evaluate the entire life cycle of bearings from normal operation to fault and degradation. The HI evaluation result based on experimental data in Xi’an Jiaotong University is consistent with the life-cycle vibration signal of bearings, providing a scientific basis for production and equipment management and improving the prognostics technology-centered prognostics and health management (PHM).http://dx.doi.org/10.1155/2022/2741616 |
| spellingShingle | Weipeng Li Yan Cao Lijuan Li Siyu Hou The Application of Orthogonal Wavelet Transformation: Support Vector Data Description in Evaluating the Performance and Health of Bearings Discrete Dynamics in Nature and Society |
| title | The Application of Orthogonal Wavelet Transformation: Support Vector Data Description in Evaluating the Performance and Health of Bearings |
| title_full | The Application of Orthogonal Wavelet Transformation: Support Vector Data Description in Evaluating the Performance and Health of Bearings |
| title_fullStr | The Application of Orthogonal Wavelet Transformation: Support Vector Data Description in Evaluating the Performance and Health of Bearings |
| title_full_unstemmed | The Application of Orthogonal Wavelet Transformation: Support Vector Data Description in Evaluating the Performance and Health of Bearings |
| title_short | The Application of Orthogonal Wavelet Transformation: Support Vector Data Description in Evaluating the Performance and Health of Bearings |
| title_sort | application of orthogonal wavelet transformation support vector data description in evaluating the performance and health of bearings |
| url | http://dx.doi.org/10.1155/2022/2741616 |
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