Attention-Guided Residual Spatiotemporal Network with Label Regularization for Fault Diagnosis with Small Samples
Fault diagnosis is of great significance for the maintenance of rotating machinery. Deep learning is an intelligent diagnostic technique that is receiving increasing attention. To address the issues of industrial data with small samples and varying working conditions, a residual convolutional neural...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4772 |
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| author | Yanlong Xu Liming Zhang Ling Chen Tian Tan Xiaolong Wang Hongguang Xiao |
| author_facet | Yanlong Xu Liming Zhang Ling Chen Tian Tan Xiaolong Wang Hongguang Xiao |
| author_sort | Yanlong Xu |
| collection | DOAJ |
| description | Fault diagnosis is of great significance for the maintenance of rotating machinery. Deep learning is an intelligent diagnostic technique that is receiving increasing attention. To address the issues of industrial data with small samples and varying working conditions, a residual convolutional neural network based on the attention mechanism is put forward for the fault diagnosis of rotating machinery. The method incorporates channel attention and spatial attention simultaneously, implementing channel-wise recalibration for frequency-dependent feature adjustment and performing spatial context aggregation across receptive fields. Subsequently, a residual module is introduced to address the vanishing gradient problem of the model in deep network structures. In addition, LSTM is used to realize spatiotemporal feature fusion. Finally, label smoothing regularization (LSR) is proposed to balance the distributional disparities among labeled samples. The effectiveness of the method is evaluated by its application to the vibration signal data from the safe injection pump and the Case Western Reserve University (CWRU). The results show that the method has superb diagnostic accuracy and strong robustness. |
| format | Article |
| id | doaj-art-4b16f98e8c534be49d2d6a430745fc33 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-4b16f98e8c534be49d2d6a430745fc332025-08-20T04:00:50ZengMDPI AGSensors1424-82202025-08-012515477210.3390/s25154772Attention-Guided Residual Spatiotemporal Network with Label Regularization for Fault Diagnosis with Small SamplesYanlong Xu0Liming Zhang1Ling Chen2Tian Tan3Xiaolong Wang4Hongguang Xiao5School of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, ChinaSchool of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, ChinaSchool of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, ChinaSchool of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, ChinaSchool of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, ChinaSchool of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, ChinaFault diagnosis is of great significance for the maintenance of rotating machinery. Deep learning is an intelligent diagnostic technique that is receiving increasing attention. To address the issues of industrial data with small samples and varying working conditions, a residual convolutional neural network based on the attention mechanism is put forward for the fault diagnosis of rotating machinery. The method incorporates channel attention and spatial attention simultaneously, implementing channel-wise recalibration for frequency-dependent feature adjustment and performing spatial context aggregation across receptive fields. Subsequently, a residual module is introduced to address the vanishing gradient problem of the model in deep network structures. In addition, LSTM is used to realize spatiotemporal feature fusion. Finally, label smoothing regularization (LSR) is proposed to balance the distributional disparities among labeled samples. The effectiveness of the method is evaluated by its application to the vibration signal data from the safe injection pump and the Case Western Reserve University (CWRU). The results show that the method has superb diagnostic accuracy and strong robustness.https://www.mdpi.com/1424-8220/25/15/4772fault diagnosisattention mechanismresidual modulelabel smoothing regularizationconvolutional neural networksmall samples |
| spellingShingle | Yanlong Xu Liming Zhang Ling Chen Tian Tan Xiaolong Wang Hongguang Xiao Attention-Guided Residual Spatiotemporal Network with Label Regularization for Fault Diagnosis with Small Samples Sensors fault diagnosis attention mechanism residual module label smoothing regularization convolutional neural network small samples |
| title | Attention-Guided Residual Spatiotemporal Network with Label Regularization for Fault Diagnosis with Small Samples |
| title_full | Attention-Guided Residual Spatiotemporal Network with Label Regularization for Fault Diagnosis with Small Samples |
| title_fullStr | Attention-Guided Residual Spatiotemporal Network with Label Regularization for Fault Diagnosis with Small Samples |
| title_full_unstemmed | Attention-Guided Residual Spatiotemporal Network with Label Regularization for Fault Diagnosis with Small Samples |
| title_short | Attention-Guided Residual Spatiotemporal Network with Label Regularization for Fault Diagnosis with Small Samples |
| title_sort | attention guided residual spatiotemporal network with label regularization for fault diagnosis with small samples |
| topic | fault diagnosis attention mechanism residual module label smoothing regularization convolutional neural network small samples |
| url | https://www.mdpi.com/1424-8220/25/15/4772 |
| work_keys_str_mv | AT yanlongxu attentionguidedresidualspatiotemporalnetworkwithlabelregularizationforfaultdiagnosiswithsmallsamples AT limingzhang attentionguidedresidualspatiotemporalnetworkwithlabelregularizationforfaultdiagnosiswithsmallsamples AT lingchen attentionguidedresidualspatiotemporalnetworkwithlabelregularizationforfaultdiagnosiswithsmallsamples AT tiantan attentionguidedresidualspatiotemporalnetworkwithlabelregularizationforfaultdiagnosiswithsmallsamples AT xiaolongwang attentionguidedresidualspatiotemporalnetworkwithlabelregularizationforfaultdiagnosiswithsmallsamples AT hongguangxiao attentionguidedresidualspatiotemporalnetworkwithlabelregularizationforfaultdiagnosiswithsmallsamples |