BEARING FAULT DIAGNOSIS METHOD BASED ON MULTI⁃SCALE AND MULTI⁃PATH ENSEMBLE NETWORK
To address the problems such as complex convolutional neural network parameters,deep layers and weak generalization performance,a multi⁃scale multi⁃path convolutional neural network(MCS⁃CNN)based bearing fault diagnosis method cas proposed.Firstly,a multi⁃scale convolution block was proposed to redu...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2024-08-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.04.003 |
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| _version_ | 1841534079112052736 |
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| author | QI BoWei LI YuanYuan SONG LiYuan |
| author_facet | QI BoWei LI YuanYuan SONG LiYuan |
| author_sort | QI BoWei |
| collection | DOAJ |
| description | To address the problems such as complex convolutional neural network parameters,deep layers and weak generalization performance,a multi⁃scale multi⁃path convolutional neural network(MCS⁃CNN)based bearing fault diagnosis method cas proposed.Firstly,a multi⁃scale convolution block was proposed to reduce the number of network parameters and increase the network width by using depthwise convolution and pointwise convolution,so as to extract multi⁃scale features effectively.Secondly,an ensemble block was proposed to increase the network depth by connecting low⁃level and high⁃level features through multiple paths,thereby improving the diagnostic accuracy of the model.Finally,the effectiveness of the method was verified on the Case Western Reserve University bearing dataset.The results show that the fault diagnosis accuracy of the proposed method can reach 975%and 9825%in high⁃noise and cross⁃load scenarios,and the accuracy in mixed scenarios is improved by more than 15%compared to existing diagnosis methods,which prove the robustness and generalisability of the proposed method. |
| format | Article |
| id | doaj-art-58cddea05dbb47fa83e0778973ecb609 |
| 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-58cddea05dbb47fa83e0778973ecb6092025-01-15T02:45:53ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-08-014677878679314212BEARING FAULT DIAGNOSIS METHOD BASED ON MULTI⁃SCALE AND MULTI⁃PATH ENSEMBLE NETWORKQI BoWeiLI YuanYuanSONG LiYuanTo address the problems such as complex convolutional neural network parameters,deep layers and weak generalization performance,a multi⁃scale multi⁃path convolutional neural network(MCS⁃CNN)based bearing fault diagnosis method cas proposed.Firstly,a multi⁃scale convolution block was proposed to reduce the number of network parameters and increase the network width by using depthwise convolution and pointwise convolution,so as to extract multi⁃scale features effectively.Secondly,an ensemble block was proposed to increase the network depth by connecting low⁃level and high⁃level features through multiple paths,thereby improving the diagnostic accuracy of the model.Finally,the effectiveness of the method was verified on the Case Western Reserve University bearing dataset.The results show that the fault diagnosis accuracy of the proposed method can reach 975%and 9825%in high⁃noise and cross⁃load scenarios,and the accuracy in mixed scenarios is improved by more than 15%compared to existing diagnosis methods,which prove the robustness and generalisability of the proposed method.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.04.003BearingConvolution neural networkMulti-scale and multi-pathFault diagnosisEnsemble network |
| spellingShingle | QI BoWei LI YuanYuan SONG LiYuan BEARING FAULT DIAGNOSIS METHOD BASED ON MULTI⁃SCALE AND MULTI⁃PATH ENSEMBLE NETWORK Jixie qiangdu Bearing Convolution neural network Multi-scale and multi-path Fault diagnosis Ensemble network |
| title | BEARING FAULT DIAGNOSIS METHOD BASED ON MULTI⁃SCALE AND MULTI⁃PATH ENSEMBLE NETWORK |
| title_full | BEARING FAULT DIAGNOSIS METHOD BASED ON MULTI⁃SCALE AND MULTI⁃PATH ENSEMBLE NETWORK |
| title_fullStr | BEARING FAULT DIAGNOSIS METHOD BASED ON MULTI⁃SCALE AND MULTI⁃PATH ENSEMBLE NETWORK |
| title_full_unstemmed | BEARING FAULT DIAGNOSIS METHOD BASED ON MULTI⁃SCALE AND MULTI⁃PATH ENSEMBLE NETWORK |
| title_short | BEARING FAULT DIAGNOSIS METHOD BASED ON MULTI⁃SCALE AND MULTI⁃PATH ENSEMBLE NETWORK |
| title_sort | bearing fault diagnosis method based on multi⁃scale and multi⁃path ensemble network |
| topic | Bearing Convolution neural network Multi-scale and multi-path Fault diagnosis Ensemble network |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.04.003 |
| work_keys_str_mv | AT qibowei bearingfaultdiagnosismethodbasedonmultiscaleandmultipathensemblenetwork AT liyuanyuan bearingfaultdiagnosismethodbasedonmultiscaleandmultipathensemblenetwork AT songliyuan bearingfaultdiagnosismethodbasedonmultiscaleandmultipathensemblenetwork |