Health Assessment of Rolling Bearing based on Self-organizing Map and Restricted Boltzmann Machine
In order to conduct dynamic health assessment for the roller bearing and describe the dynamic process of its performance degradation accurately,by using the method of combining the Self-organizing map（ SOM） and restricted Boltzmann machine（ RBM） to implement the health assessment of rolling bearing....
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2017-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.06.005 |
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| author | Liu Hao Xiong Xin Wang Xiaojing Guo Jiayu Shen Jiexi |
| author_facet | Liu Hao Xiong Xin Wang Xiaojing Guo Jiayu Shen Jiexi |
| author_sort | Liu Hao |
| collection | DOAJ |
| description | In order to conduct dynamic health assessment for the roller bearing and describe the dynamic process of its performance degradation accurately,by using the method of combining the Self-organizing map（ SOM） and restricted Boltzmann machine（ RBM） to implement the health assessment of rolling bearing. With the consideration of degradation induced changes for response features,unsupervised learning scheme based on SOM,with the help of multi-domain feature sets consist of features in time domain,frequency domain and time-frequency domain,the optimal feature domain is constructed by sorting the diverse features adopting the sequential forward selection（ SFS） regulation,the mapping relationship between the selected feature vectors and bearing health status is obtained. To avoid the problems of being easy to be run into local optimum,parameter adjustment difficulties and a long training process,when conducting the learning process using traditional neural network algorithms,the model for health assessment is constructed by using the RBM as an alternative.Experimental results demonstrate that the proposed method can identify the health status of rolling bearing during the dynamic process of performance degradation with a good engineering applicability. |
| format | Article |
| id | doaj-art-3466d559eebf47c18618bf0c58c0d51d |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2017-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-3466d559eebf47c18618bf0c58c0d51d2025-01-10T14:22:54ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392017-01-0141252929931017Health Assessment of Rolling Bearing based on Self-organizing Map and Restricted Boltzmann MachineLiu HaoXiong XinWang XiaojingGuo JiayuShen JiexiIn order to conduct dynamic health assessment for the roller bearing and describe the dynamic process of its performance degradation accurately,by using the method of combining the Self-organizing map（ SOM） and restricted Boltzmann machine（ RBM） to implement the health assessment of rolling bearing. With the consideration of degradation induced changes for response features,unsupervised learning scheme based on SOM,with the help of multi-domain feature sets consist of features in time domain,frequency domain and time-frequency domain,the optimal feature domain is constructed by sorting the diverse features adopting the sequential forward selection（ SFS） regulation,the mapping relationship between the selected feature vectors and bearing health status is obtained. To avoid the problems of being easy to be run into local optimum,parameter adjustment difficulties and a long training process,when conducting the learning process using traditional neural network algorithms,the model for health assessment is constructed by using the RBM as an alternative.Experimental results demonstrate that the proposed method can identify the health status of rolling bearing during the dynamic process of performance degradation with a good engineering applicability.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.06.005Sequential forward algorithmSelf-organizing mapRestricted Boltzmann machineHealth assessmentRolling bearing |
| spellingShingle | Liu Hao Xiong Xin Wang Xiaojing Guo Jiayu Shen Jiexi Health Assessment of Rolling Bearing based on Self-organizing Map and Restricted Boltzmann Machine Jixie chuandong Sequential forward algorithm Self-organizing map Restricted Boltzmann machine Health assessment Rolling bearing |
| title | Health Assessment of Rolling Bearing based on Self-organizing Map and Restricted Boltzmann Machine |
| title_full | Health Assessment of Rolling Bearing based on Self-organizing Map and Restricted Boltzmann Machine |
| title_fullStr | Health Assessment of Rolling Bearing based on Self-organizing Map and Restricted Boltzmann Machine |
| title_full_unstemmed | Health Assessment of Rolling Bearing based on Self-organizing Map and Restricted Boltzmann Machine |
| title_short | Health Assessment of Rolling Bearing based on Self-organizing Map and Restricted Boltzmann Machine |
| title_sort | health assessment of rolling bearing based on self organizing map and restricted boltzmann machine |
| topic | Sequential forward algorithm Self-organizing map Restricted Boltzmann machine Health assessment Rolling bearing |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.06.005 |
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