Multi-frequency eddy current testing signal fusion using a complex-valued long short-term memory neural network.
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| Title: | Multi-frequency eddy current testing signal fusion using a complex-valued long short-term memory neural network. |
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| Authors: | Chen, Bing1, Yu, Tengwei1, Bai, Ling2, Tsukada, Kazuhiko3, Chen, Renxiang1, Liu, Zheng2 |
| Source: | Insight: Non-Destructive Testing & Condition Monitoring. Jan2026, Vol. 68 Issue 1, p20-30. 11p. |
| Subjects: | Eddy current testing, Long short-term memory, Artificial neural networks, Signal processing, Multisensor data fusion, Nondestructive testing |
| Abstract: | Eddy current testing (ECT) is widely applied to industry applications such as manufacturing and infrastructure condition assessment and maintenance. However, due to varied sources of interference associated with the inspection process, variations introduced to the acquired ECT signals may lead to the degradation of signal quality for defect characterisation. Conventional ECT signal processing approaches cannot address these issues properly, as they can hardly capture the complex features of signals efficiently and are less robust against noise and interference. Thus, this study proposes an ECT signal fusion approach using a complex-valued long short-term memory (CVLSTM) neural network. This method can enhance signal quality and improve defect characterisation capabilities by fusing signals at different frequencies. The experimental results demonstrate the advantages of multi-frequency ECT signal fusion in terms of signal quality metrics. Compared to real-valued networks, the complex-valued computations inherently preserve both amplitude and phase relationships, thereby producing fused signals with significantly enhanced quality. Thus, the paper highlights the potential of complex-valued neural networks for ECT signal processing and data analysis. [ABSTRACT FROM AUTHOR] |
| Copyright of Insight: Non-Destructive Testing & Condition Monitoring is the property of British Institute of Non-Destructive Testing 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191436109 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi-frequency eddy current testing signal fusion using a complex-valued long short-term memory neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Bing%22">Chen, Bing</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yu%2C+Tengwei%22">Yu, Tengwei</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Bai%2C+Ling%22">Bai, Ling</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Tsukada%2C+Kazuhiko%22">Tsukada, Kazuhiko</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Chen%2C+Renxiang%22">Chen, Renxiang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Liu%2C+Zheng%22">Liu, Zheng</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Insight%3A+Non-Destructive+Testing+%26+Condition+Monitoring%22">Insight: Non-Destructive Testing & Condition Monitoring</searchLink>. Jan2026, Vol. 68 Issue 1, p20-30. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Eddy+current+testing%22">Eddy current testing</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Nondestructive+testing%22">Nondestructive testing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Eddy current testing (ECT) is widely applied to industry applications such as manufacturing and infrastructure condition assessment and maintenance. However, due to varied sources of interference associated with the inspection process, variations introduced to the acquired ECT signals may lead to the degradation of signal quality for defect characterisation. Conventional ECT signal processing approaches cannot address these issues properly, as they can hardly capture the complex features of signals efficiently and are less robust against noise and interference. Thus, this study proposes an ECT signal fusion approach using a complex-valued long short-term memory (CVLSTM) neural network. This method can enhance signal quality and improve defect characterisation capabilities by fusing signals at different frequencies. The experimental results demonstrate the advantages of multi-frequency ECT signal fusion in terms of signal quality metrics. Compared to real-valued networks, the complex-valued computations inherently preserve both amplitude and phase relationships, thereby producing fused signals with significantly enhanced quality. Thus, the paper highlights the potential of complex-valued neural networks for ECT signal processing and data analysis. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Insight: Non-Destructive Testing & Condition Monitoring is the property of British Institute of Non-Destructive Testing 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: 10.1784/insi.2026.68.1.20 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 20 Subjects: – SubjectFull: Eddy current testing Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Signal processing Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Nondestructive testing Type: general Titles: – TitleFull: Multi-frequency eddy current testing signal fusion using a complex-valued long short-term memory neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Bing – PersonEntity: Name: NameFull: Yu, Tengwei – PersonEntity: Name: NameFull: Bai, Ling – PersonEntity: Name: NameFull: Tsukada, Kazuhiko – PersonEntity: Name: NameFull: Chen, Renxiang – PersonEntity: Name: NameFull: Liu, Zheng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13542575 Numbering: – Type: volume Value: 68 – Type: issue Value: 1 Titles: – TitleFull: Insight: Non-Destructive Testing & Condition Monitoring Type: main |
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