RS-SCBiGRU: a noise-robust neural network for high-speed motor fault diagnosis with limited samples
Abstract Convolutional Neural Networks, with their excellent capabilities for automatic feature discrimination and learning, have been widely applied in the field of mechanical fault diagnosis. However, in real-world operating environments, acquiring large amounts of fault data as training samples i...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-02500-2 |
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| author | Sun Fenghao Li Guofa He Jialong Liu Shaoyang |
| author_facet | Sun Fenghao Li Guofa He Jialong Liu Shaoyang |
| author_sort | Sun Fenghao |
| collection | DOAJ |
| description | Abstract Convolutional Neural Networks, with their excellent capabilities for automatic feature discrimination and learning, have been widely applied in the field of mechanical fault diagnosis. However, in real-world operating environments, acquiring large amounts of fault data as training samples is often challenging, which limits the applicability of traditional methods. To address this issue, this study proposes a frequency-adaptive fault diagnosis method for high-speed motors under small-sample scenarios. Specifically, this paper designs an innovative data augmentation technique that effectively expands the diversity and coverage of the training dataset and is seamlessly integrated into the fault diagnosis model. Furthermore, to enhance the richness of feature representations and strengthen information exchange between different feature channels, this paper proposes a frequency-adaptive convolutional layer (SCNET), which significantly optimizes the performance of Bidirectional Gated Recurrent Units (BiGRU) in fault feature extraction. Based on these technological improvements, we have constructed an efficient intelligent fault diagnosis model named RS-SCBiGRU. Experimental validation shows that, compared to various advanced fault diagnosis methods, the RS-SCBiGRU model achieves a significant improvement in accuracy and demonstrates stronger noise resistance capabilities. |
| format | Article |
| id | doaj-art-5d313d218fc44d3c9958b6c9bf010ba7 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5d313d218fc44d3c9958b6c9bf010ba72025-08-20T03:03:42ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-02500-2RS-SCBiGRU: a noise-robust neural network for high-speed motor fault diagnosis with limited samplesSun Fenghao0Li Guofa1He Jialong2Liu Shaoyang3Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin UniversityKey Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin UniversityKey Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin UniversityKey Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin UniversityAbstract Convolutional Neural Networks, with their excellent capabilities for automatic feature discrimination and learning, have been widely applied in the field of mechanical fault diagnosis. However, in real-world operating environments, acquiring large amounts of fault data as training samples is often challenging, which limits the applicability of traditional methods. To address this issue, this study proposes a frequency-adaptive fault diagnosis method for high-speed motors under small-sample scenarios. Specifically, this paper designs an innovative data augmentation technique that effectively expands the diversity and coverage of the training dataset and is seamlessly integrated into the fault diagnosis model. Furthermore, to enhance the richness of feature representations and strengthen information exchange between different feature channels, this paper proposes a frequency-adaptive convolutional layer (SCNET), which significantly optimizes the performance of Bidirectional Gated Recurrent Units (BiGRU) in fault feature extraction. Based on these technological improvements, we have constructed an efficient intelligent fault diagnosis model named RS-SCBiGRU. Experimental validation shows that, compared to various advanced fault diagnosis methods, the RS-SCBiGRU model achieves a significant improvement in accuracy and demonstrates stronger noise resistance capabilities.https://doi.org/10.1038/s41598-025-02500-2Fault diagnosisSmall sampleData augmentationSelf-calibrating convolutionBidirectional gated recurrent unit |
| spellingShingle | Sun Fenghao Li Guofa He Jialong Liu Shaoyang RS-SCBiGRU: a noise-robust neural network for high-speed motor fault diagnosis with limited samples Scientific Reports Fault diagnosis Small sample Data augmentation Self-calibrating convolution Bidirectional gated recurrent unit |
| title | RS-SCBiGRU: a noise-robust neural network for high-speed motor fault diagnosis with limited samples |
| title_full | RS-SCBiGRU: a noise-robust neural network for high-speed motor fault diagnosis with limited samples |
| title_fullStr | RS-SCBiGRU: a noise-robust neural network for high-speed motor fault diagnosis with limited samples |
| title_full_unstemmed | RS-SCBiGRU: a noise-robust neural network for high-speed motor fault diagnosis with limited samples |
| title_short | RS-SCBiGRU: a noise-robust neural network for high-speed motor fault diagnosis with limited samples |
| title_sort | rs scbigru a noise robust neural network for high speed motor fault diagnosis with limited samples |
| topic | Fault diagnosis Small sample Data augmentation Self-calibrating convolution Bidirectional gated recurrent unit |
| url | https://doi.org/10.1038/s41598-025-02500-2 |
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