FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2
Traditional fault diagnosis methods for rolling bearings are difficult to accurately and efficiently achieve fault classification.A method of rolling bearing fault classification based on symmetrized dot pattern(SDP)and improved SAM⁃MobileNetv2 was proposed.Firstly,the bearing vibration signal was t...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2024-08-01
|
| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.04.004 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841534083996319744 |
|---|---|
| author | ZHANG TianYuan SUN HuEr ZHU JiYang ZHAO Yang |
| author_facet | ZHANG TianYuan SUN HuEr ZHU JiYang ZHAO Yang |
| author_sort | ZHANG TianYuan |
| collection | DOAJ |
| description | Traditional fault diagnosis methods for rolling bearings are difficult to accurately and efficiently achieve fault classification.A method of rolling bearing fault classification based on symmetrized dot pattern(SDP)and improved SAM⁃MobileNetv2 was proposed.Firstly,the bearing vibration signal was transformed into two⁃dimensional images with rich characteristic information by SDP algorithm.Secondly,the two⁃dimensional images were fed into the SAM⁃MobileNetv2 network model,which extracted and classified fault feature information.Improved SAM⁃MobileNetv2 networks used the adaptive activation function ACON to replace the ReLU6 activation function in SAM⁃MobileNetv2 to improve model classification performance.Finally,this model was compared with various models.The experimental results show that this model can accurately and efficiently realize the classification of rolling bearing faults,using Case Western Reserve University bearing fault data with an accuracy rate of 99.5%,using the University of Ottawa bearing failure data with an accuracy rate of 97.2%. |
| format | Article |
| id | doaj-art-6609b94a825e4b62a7f2355beff680e9 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-6609b94a825e4b62a7f2355beff680e92025-01-15T02:45:53ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-08-014678779479314224FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2ZHANG TianYuanSUN HuErZHU JiYangZHAO YangTraditional fault diagnosis methods for rolling bearings are difficult to accurately and efficiently achieve fault classification.A method of rolling bearing fault classification based on symmetrized dot pattern(SDP)and improved SAM⁃MobileNetv2 was proposed.Firstly,the bearing vibration signal was transformed into two⁃dimensional images with rich characteristic information by SDP algorithm.Secondly,the two⁃dimensional images were fed into the SAM⁃MobileNetv2 network model,which extracted and classified fault feature information.Improved SAM⁃MobileNetv2 networks used the adaptive activation function ACON to replace the ReLU6 activation function in SAM⁃MobileNetv2 to improve model classification performance.Finally,this model was compared with various models.The experimental results show that this model can accurately and efficiently realize the classification of rolling bearing faults,using Case Western Reserve University bearing fault data with an accuracy rate of 99.5%,using the University of Ottawa bearing failure data with an accuracy rate of 97.2%.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.04.004Rolling bearingSymmetrized dot patternSAM-MobileNetv2 modelFault diagnosis |
| spellingShingle | ZHANG TianYuan SUN HuEr ZHU JiYang ZHAO Yang FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2 Jixie qiangdu Rolling bearing Symmetrized dot pattern SAM-MobileNetv2 model Fault diagnosis |
| title | FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2 |
| title_full | FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2 |
| title_fullStr | FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2 |
| title_full_unstemmed | FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2 |
| title_short | FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2 |
| title_sort | fault diagnosis of rolling bearings based on sdp and improved sam⁃mobilenetv2 |
| topic | Rolling bearing Symmetrized dot pattern SAM-MobileNetv2 model Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.04.004 |
| work_keys_str_mv | AT zhangtianyuan faultdiagnosisofrollingbearingsbasedonsdpandimprovedsammobilenetv2 AT sunhuer faultdiagnosisofrollingbearingsbasedonsdpandimprovedsammobilenetv2 AT zhujiyang faultdiagnosisofrollingbearingsbasedonsdpandimprovedsammobilenetv2 AT zhaoyang faultdiagnosisofrollingbearingsbasedonsdpandimprovedsammobilenetv2 |