Conv-Transformer based few-shot learning for highly accurate multi-task structural health monitoring via piezoelectric impedance.
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| Title: | Conv-Transformer based few-shot learning for highly accurate multi-task structural health monitoring via piezoelectric impedance. |
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| Authors: | Sun, Hanqiao1 (AUTHOR), Lu, Jingfeng1 (AUTHOR), Xu, Jiawen1 (AUTHOR) jiawen.xu@seu.edu.cn, Yan, Ruqiang2 (AUTHOR) |
| Source: | Mechanical Systems & Signal Processing. Mar2026, Vol. 247, pN.PAG-N.PAG. 1p. |
| Subjects: | Structural health monitoring, Transformer models, Feature extraction, Fault diagnosis, Machine learning, Piezoelectric transducers, Impedance spectroscopy |
| Abstract: | Impedance signals for structural health monitoring are often sparse and difficult to acquire in damaged conditions. Increasing the damage categories would significantly reduce accuracy. In this study, we propose a Conv-Transformer model that is capable of multi-task structural health monitoring, addressing the complexities of small sample datasets while handling multiple fault detection tasks, including mass loss and bolt loosening. The model enhances feature extraction by combining convolutional layers and multi-head attention within the Transformer encoder, focusing on the relative location of the peaks and the local feature of each peak in the impedance signals. These advantages enable highly accurate multi-task SHM with small samples of impedance signals. The proposed model is first trained on a large amount of data in mixed conditions and then fine-tuned with small sample data for an eight-class fault classification task. Experimental results show that the model demonstrates strong learning ability and cross-condition transferability, achieving an accuracy of 92.12% for multi-task damage identification, a 4.49% improvement over a conventional Transformer baseline. The proposed method can be applied to health conditions identification of buildings, bridges, and trusses. [ABSTRACT FROM AUTHOR] |
| Copyright of Mechanical Systems & Signal Processing is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191526308 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Conv-Transformer based few-shot learning for highly accurate multi-task structural health monitoring via piezoelectric impedance. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sun%2C+Hanqiao%22">Sun, Hanqiao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Jingfeng%22">Lu, Jingfeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Jiawen%22">Xu, Jiawen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jiawen.xu@seu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yan%2C+Ruqiang%22">Yan, Ruqiang</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mechanical+Systems+%26+Signal+Processing%22">Mechanical Systems & Signal Processing</searchLink>. Mar2026, Vol. 247, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Structural+health+monitoring%22">Structural health monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Piezoelectric+transducers%22">Piezoelectric transducers</searchLink><br /><searchLink fieldCode="DE" term="%22Impedance+spectroscopy%22">Impedance spectroscopy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Impedance signals for structural health monitoring are often sparse and difficult to acquire in damaged conditions. Increasing the damage categories would significantly reduce accuracy. In this study, we propose a Conv-Transformer model that is capable of multi-task structural health monitoring, addressing the complexities of small sample datasets while handling multiple fault detection tasks, including mass loss and bolt loosening. The model enhances feature extraction by combining convolutional layers and multi-head attention within the Transformer encoder, focusing on the relative location of the peaks and the local feature of each peak in the impedance signals. These advantages enable highly accurate multi-task SHM with small samples of impedance signals. The proposed model is first trained on a large amount of data in mixed conditions and then fine-tuned with small sample data for an eight-class fault classification task. Experimental results show that the model demonstrates strong learning ability and cross-condition transferability, achieving an accuracy of 92.12% for multi-task damage identification, a 4.49% improvement over a conventional Transformer baseline. The proposed method can be applied to health conditions identification of buildings, bridges, and trusses. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mechanical Systems & Signal Processing is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.ymssp.2026.113967 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Structural health monitoring Type: general – SubjectFull: Transformer models Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Fault diagnosis Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Piezoelectric transducers Type: general – SubjectFull: Impedance spectroscopy Type: general Titles: – TitleFull: Conv-Transformer based few-shot learning for highly accurate multi-task structural health monitoring via piezoelectric impedance. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sun, Hanqiao – PersonEntity: Name: NameFull: Lu, Jingfeng – PersonEntity: Name: NameFull: Xu, Jiawen – PersonEntity: Name: NameFull: Yan, Ruqiang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08883270 Numbering: – Type: volume Value: 247 Titles: – TitleFull: Mechanical Systems & Signal Processing Type: main |
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