Auto-embedding transformer under multi-source information fusion for few-shot fault diagnosis
Abstract Data-driven intelligent fault diagnosis methods have become essential for ensuring the reliability and stability of mechanical systems. However, their practical application is often hindered by the scarcity of labeled samples and the absence of effective multi-source information fusion stra...
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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/s41598-025-10124-9 |
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| author | Bo Wang Shuai Zhao Qian Zhao Yang Bai |
| author_facet | Bo Wang Shuai Zhao Qian Zhao Yang Bai |
| author_sort | Bo Wang |
| collection | DOAJ |
| description | Abstract Data-driven intelligent fault diagnosis methods have become essential for ensuring the reliability and stability of mechanical systems. However, their practical application is often hindered by the scarcity of labeled samples and the absence of effective multi-source information fusion strategies, which collectively limit the accuracy of existing fault diagnosis frameworks. To address these challenges, we propose a novel auto-embedding transformer named EDformer, tailored for multi-source information under few-shot fault diagnosis. First, the multi-source information is fed into a novel encoder–decoder to extract high-quality embeddings, thereby mitigating the challenges posed by limited samples in real-world engineering applications. Subsequently, an innovative cross-attention architecture leveraging Transformer neural networks is proposed to facilitate efficient multi-modal data integration by highlighting key correlations between sensing devices while minimizing superfluous information. In the final stage, the architecture integrates global max pooling and global average pooling operations to optimize feature abstraction and improve resilience to data variations. The effectiveness of the proposed framework is validated through comprehensive evaluations on two heterogeneous datasets. Diagnostic results demonstrate that EDformer surpasses contemporary approaches in both classification accuracy and stability, particularly under conditions of data scarcity. Visualization tools such as t-SNE and ROC curves further confirm its ability to effectively distinguish fault categories and capture critical fault-related features. |
| format | Article |
| id | doaj-art-7ba0eccfc05f47248c42debfdf68b0ba |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7ba0eccfc05f47248c42debfdf68b0ba2025-08-20T03:42:25ZengNature PortfolioScientific Reports2045-23222025-07-0115111710.1038/s41598-025-10124-9Auto-embedding transformer under multi-source information fusion for few-shot fault diagnosisBo Wang0Shuai Zhao1Qian Zhao2Yang Bai3School of Information and Artificial Intelligence, Nanchang Institute of Science & TechnologySchool of Information and Artificial Intelligence, Nanchang Institute of Science & TechnologyDepartment of Aerospace Science and Technology, Politecnico di MilanoSchool of Education, Nanchang Institute of Science & TechnologyAbstract Data-driven intelligent fault diagnosis methods have become essential for ensuring the reliability and stability of mechanical systems. However, their practical application is often hindered by the scarcity of labeled samples and the absence of effective multi-source information fusion strategies, which collectively limit the accuracy of existing fault diagnosis frameworks. To address these challenges, we propose a novel auto-embedding transformer named EDformer, tailored for multi-source information under few-shot fault diagnosis. First, the multi-source information is fed into a novel encoder–decoder to extract high-quality embeddings, thereby mitigating the challenges posed by limited samples in real-world engineering applications. Subsequently, an innovative cross-attention architecture leveraging Transformer neural networks is proposed to facilitate efficient multi-modal data integration by highlighting key correlations between sensing devices while minimizing superfluous information. In the final stage, the architecture integrates global max pooling and global average pooling operations to optimize feature abstraction and improve resilience to data variations. The effectiveness of the proposed framework is validated through comprehensive evaluations on two heterogeneous datasets. Diagnostic results demonstrate that EDformer surpasses contemporary approaches in both classification accuracy and stability, particularly under conditions of data scarcity. Visualization tools such as t-SNE and ROC curves further confirm its ability to effectively distinguish fault categories and capture critical fault-related features.https://doi.org/10.1038/s41598-025-10124-9Fault diagnosisEncoder–decoderMulti-source information fusionTransformer |
| spellingShingle | Bo Wang Shuai Zhao Qian Zhao Yang Bai Auto-embedding transformer under multi-source information fusion for few-shot fault diagnosis Scientific Reports Fault diagnosis Encoder–decoder Multi-source information fusion Transformer |
| title | Auto-embedding transformer under multi-source information fusion for few-shot fault diagnosis |
| title_full | Auto-embedding transformer under multi-source information fusion for few-shot fault diagnosis |
| title_fullStr | Auto-embedding transformer under multi-source information fusion for few-shot fault diagnosis |
| title_full_unstemmed | Auto-embedding transformer under multi-source information fusion for few-shot fault diagnosis |
| title_short | Auto-embedding transformer under multi-source information fusion for few-shot fault diagnosis |
| title_sort | auto embedding transformer under multi source information fusion for few shot fault diagnosis |
| topic | Fault diagnosis Encoder–decoder Multi-source information fusion Transformer |
| url | https://doi.org/10.1038/s41598-025-10124-9 |
| work_keys_str_mv | AT bowang autoembeddingtransformerundermultisourceinformationfusionforfewshotfaultdiagnosis AT shuaizhao autoembeddingtransformerundermultisourceinformationfusionforfewshotfaultdiagnosis AT qianzhao autoembeddingtransformerundermultisourceinformationfusionforfewshotfaultdiagnosis AT yangbai autoembeddingtransformerundermultisourceinformationfusionforfewshotfaultdiagnosis |