Efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics
Abstract Convolutional Neural Networks have been widely applied in fault diagnosis tasks of mechanical systems due to their strong feature extraction and classification capabilities. However, they have limitations in handling global context information. Vision Transformers, by leveraging self-attent...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88668-z |
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| author | Chaoquan Mo Ke Huang Houxin Ji Wenhan Li Kaibo Xu |
| author_facet | Chaoquan Mo Ke Huang Houxin Ji Wenhan Li Kaibo Xu |
| author_sort | Chaoquan Mo |
| collection | DOAJ |
| description | Abstract Convolutional Neural Networks have been widely applied in fault diagnosis tasks of mechanical systems due to their strong feature extraction and classification capabilities. However, they have limitations in handling global context information. Vision Transformers, by leveraging self-attention mechanisms to capture global dependencies, have shown excellent performance in many visual tasks, but often come with high computational costs. Therefore, this paper proposes a lightweight and efficient intelligent fault diagnosis method based on the fusion of Convolutional Network and Vision Transformer features (FCNVT). This method combines the local feature extraction capability of CNNs with the global dependency capturing ability of ViTs, while maintaining computational efficiency. Random overlapping sampling (ROS) techniques are used to preprocess signals, generating two-dimensional synchronized wavelet transform (SWT) images as inputs to the network. Experimental verification has shown that the proposed method achieves up to 100% classification accuracy, with the model having 7 million parameters and a computational cost of only 0.28 G, outperforming other state-of-the-art methods. Finally, a graphical user interface (GUI)-based mechanical equipment fault detection system was developed using this method, which holds positive implications for advancing the practical application of intelligent fault diagnosis in mechanical equipment. |
| format | Article |
| id | doaj-art-a4c504b0ab144e25b91f174d9e73e5b2 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a4c504b0ab144e25b91f174d9e73e5b22025-08-20T03:00:39ZengNature PortfolioScientific Reports2045-23222025-02-0115111810.1038/s41598-025-88668-zEfficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristicsChaoquan Mo0Ke Huang1Houxin Ji2Wenhan Li3Kaibo Xu4College of Mechanical & Electrical Engineering, Wenzhou UniversityCollege of Mechanical & Electrical Engineering, Wenzhou UniversityCollege of Mechanical & Electrical Engineering, Wenzhou UniversityCollege of Mechanical & Electrical Engineering, Wenzhou UniversityCollege of Mechanical & Electrical Engineering, Wenzhou UniversityAbstract Convolutional Neural Networks have been widely applied in fault diagnosis tasks of mechanical systems due to their strong feature extraction and classification capabilities. However, they have limitations in handling global context information. Vision Transformers, by leveraging self-attention mechanisms to capture global dependencies, have shown excellent performance in many visual tasks, but often come with high computational costs. Therefore, this paper proposes a lightweight and efficient intelligent fault diagnosis method based on the fusion of Convolutional Network and Vision Transformer features (FCNVT). This method combines the local feature extraction capability of CNNs with the global dependency capturing ability of ViTs, while maintaining computational efficiency. Random overlapping sampling (ROS) techniques are used to preprocess signals, generating two-dimensional synchronized wavelet transform (SWT) images as inputs to the network. Experimental verification has shown that the proposed method achieves up to 100% classification accuracy, with the model having 7 million parameters and a computational cost of only 0.28 G, outperforming other state-of-the-art methods. Finally, a graphical user interface (GUI)-based mechanical equipment fault detection system was developed using this method, which holds positive implications for advancing the practical application of intelligent fault diagnosis in mechanical equipment.https://doi.org/10.1038/s41598-025-88668-zMechanical equipment fault diagnosisCNN-transformerRandom overlapping samplingSynchrosqueezed wavelet transformGraphical user interface |
| spellingShingle | Chaoquan Mo Ke Huang Houxin Ji Wenhan Li Kaibo Xu Efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics Scientific Reports Mechanical equipment fault diagnosis CNN-transformer Random overlapping sampling Synchrosqueezed wavelet transform Graphical user interface |
| title | Efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics |
| title_full | Efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics |
| title_fullStr | Efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics |
| title_full_unstemmed | Efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics |
| title_short | Efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics |
| title_sort | efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics |
| topic | Mechanical equipment fault diagnosis CNN-transformer Random overlapping sampling Synchrosqueezed wavelet transform Graphical user interface |
| url | https://doi.org/10.1038/s41598-025-88668-z |
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