FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS
Since the fault vibration data collected in real engineering may be accompanied by noise，traditional diagnostic models are difficult to identify fault categories，to address this problem，a rolling bearing fault diagnosis research method based on channel and spatial reconstruction and progressive conv...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2024-01-01
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| Series: | Jixie qiangdu |
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| Online Access: | http://www.jxqd.net.cn/thesisDetails?columnId=55092450&Fpath=home&index=0 |
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| _version_ | 1841534179957800960 |
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| author | ZHOU Tao YAO DeChen YANG JianWei |
| author_facet | ZHOU Tao YAO DeChen YANG JianWei |
| author_sort | ZHOU Tao |
| collection | DOAJ |
| description | Since the fault vibration data collected in real engineering may be accompanied by noise，traditional diagnostic models are difficult to identify fault categories，to address this problem，a rolling bearing fault diagnosis research method based on channel and spatial reconstruction and progressive convolutional neural networks （CSRP-CNN） was proposed.The model utilizes channel and spatial reconstruction convolution （CSConv） to reduce the redundant information of channels and space in fault features，and reduces the complexity and computation to improve the performance； using convolutional block attention module （CBAM），attention enhancement operation was carried out in the channel and spatial dimensions to make the model pay attention to important fault feature information； and progressive convolutional network structure was used in the shallow layer of the network，which will fuse the previous fault feature information fused with the current input to obtain richer feature information.The performance of CSRP-CNN was evaluated by two different datasets of Case Western Reserve University（CWRU）and machinery fault simulator magnum（MFS-MG）.After the noise and ablation tests，it is verified that CSRP-CNN has strong robustness and the effects of CSConv，CBAM and progressive convolutional neural network（PCNN） on the model noise immunity performance. |
| format | Article |
| id | doaj-art-7a3e3c7f9f604a23b52b0e33863d5f0e |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-7a3e3c7f9f604a23b52b0e33863d5f0e2025-01-15T02:44:34ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-01-0111055092450FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKSZHOU TaoYAO DeChenYANG JianWeiSince the fault vibration data collected in real engineering may be accompanied by noise，traditional diagnostic models are difficult to identify fault categories，to address this problem，a rolling bearing fault diagnosis research method based on channel and spatial reconstruction and progressive convolutional neural networks （CSRP-CNN） was proposed.The model utilizes channel and spatial reconstruction convolution （CSConv） to reduce the redundant information of channels and space in fault features，and reduces the complexity and computation to improve the performance； using convolutional block attention module （CBAM），attention enhancement operation was carried out in the channel and spatial dimensions to make the model pay attention to important fault feature information； and progressive convolutional network structure was used in the shallow layer of the network，which will fuse the previous fault feature information fused with the current input to obtain richer feature information.The performance of CSRP-CNN was evaluated by two different datasets of Case Western Reserve University（CWRU）and machinery fault simulator magnum（MFS-MG）.After the noise and ablation tests，it is verified that CSRP-CNN has strong robustness and the effects of CSConv，CBAM and progressive convolutional neural network（PCNN） on the model noise immunity performance.http://www.jxqd.net.cn/thesisDetails?columnId=55092450&Fpath=home&index=0Fault diagnosisChannel and spatial reorganization convolutionProgressive convolutional neural networkRobustness |
| spellingShingle | ZHOU Tao YAO DeChen YANG JianWei FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS Jixie qiangdu Fault diagnosis Channel and spatial reorganization convolution Progressive convolutional neural network Robustness |
| title | FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS |
| title_full | FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS |
| title_fullStr | FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS |
| title_full_unstemmed | FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS |
| title_short | FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS |
| title_sort | fault diagnosis of rolling bearings based on channel and spatial reconstruction networks |
| topic | Fault diagnosis Channel and spatial reorganization convolution Progressive convolutional neural network Robustness |
| url | http://www.jxqd.net.cn/thesisDetails?columnId=55092450&Fpath=home&index=0 |
| work_keys_str_mv | AT zhoutao faultdiagnosisofrollingbearingsbasedonchannelandspatialreconstructionnetworks AT yaodechen faultdiagnosisofrollingbearingsbasedonchannelandspatialreconstructionnetworks AT yangjianwei faultdiagnosisofrollingbearingsbasedonchannelandspatialreconstructionnetworks |