Dynamic Anomaly Detection: A Multimodal Spatiotemporal Cross-Fusion Transformer Approach.
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| Title: | Dynamic Anomaly Detection: A Multimodal Spatiotemporal Cross-Fusion Transformer Approach. |
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| Authors: | Deng, Zefu1 (AUTHOR), Xu, Dongliang2 (AUTHOR) 61265@njnu.edu.cn |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 11, p2605. 20p. |
| Subject Terms: | *Fault diagnosis, *Multisensor data fusion, *Outlier detection, *Rotating machinery, *Deep learning, *Transformer models, *Industrial equipment |
| Abstract: | Accurate fault diagnosis is critical for the reliable and secure operation of modern industrial equipment; however, traditional unimodal data representations often fail to fully capture the complex, multi-dimensional characteristics of mechanical faults. To address this limitation, this paper proposes CFNET, a novel multimodal cross-fusion recognition framework designed for the robust fault diagnosis of rotating machinery, including vane pumps and bearings. The proposed framework employs an innovative three-modal input strategy that systematically extracts deep spatial and temporal features using parallel ResNet50 and stacked GRU networks. To break through the limitations of conventional decision-level fusion, CFNET introduces a dynamic mid-layer cross-fusion mechanism driven by adaptive attention weights, effectively enhancing feature interaction depth while preventing redundancy during back-propagation. Furthermore, handcrafted prior features are integrated via skip connections to supplement the deep learning representations. The aggregated multi-dimensional features are ultimately processed by a Transformer architecture for feature sparsification and high-precision classification. Extensive experiments rigorously validate the superiority of the proposed method. On a custom-built industrial vane pump test rig, CFNET achieved a maximum accuracy improvement of 25% over traditional unimodal methods. Furthermore, it demonstrated state-of-the-art performance on two authoritative benchmark datasets, achieving impressive recognition accuracies of 98.12% and 99.92% on the Paderborn University and Case Western Reserve University bearing datasets, respectively. These results underscore the exceptional robustness, efficiency, and industrial applicability of the CFNET framework. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194587993 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dynamic Anomaly Detection: A Multimodal Spatiotemporal Cross-Fusion Transformer Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Deng%2C+Zefu%22">Deng, Zefu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Dongliang%22">Xu, Dongliang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> 61265@njnu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 11, p2605. 20p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br />*<searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br />*<searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br />*<searchLink fieldCode="DE" term="%22Rotating+machinery%22">Rotating machinery</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br />*<searchLink fieldCode="DE" term="%22Industrial+equipment%22">Industrial equipment</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate fault diagnosis is critical for the reliable and secure operation of modern industrial equipment; however, traditional unimodal data representations often fail to fully capture the complex, multi-dimensional characteristics of mechanical faults. To address this limitation, this paper proposes CFNET, a novel multimodal cross-fusion recognition framework designed for the robust fault diagnosis of rotating machinery, including vane pumps and bearings. The proposed framework employs an innovative three-modal input strategy that systematically extracts deep spatial and temporal features using parallel ResNet50 and stacked GRU networks. To break through the limitations of conventional decision-level fusion, CFNET introduces a dynamic mid-layer cross-fusion mechanism driven by adaptive attention weights, effectively enhancing feature interaction depth while preventing redundancy during back-propagation. Furthermore, handcrafted prior features are integrated via skip connections to supplement the deep learning representations. The aggregated multi-dimensional features are ultimately processed by a Transformer architecture for feature sparsification and high-precision classification. Extensive experiments rigorously validate the superiority of the proposed method. On a custom-built industrial vane pump test rig, CFNET achieved a maximum accuracy improvement of 25% over traditional unimodal methods. Furthermore, it demonstrated state-of-the-art performance on two authoritative benchmark datasets, achieving impressive recognition accuracies of 98.12% and 99.92% on the Paderborn University and Case Western Reserve University bearing datasets, respectively. These results underscore the exceptional robustness, efficiency, and industrial applicability of the CFNET framework. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194587993 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19112605 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 2605 Subjects: – SubjectFull: Fault diagnosis Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Outlier detection Type: general – SubjectFull: Rotating machinery Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Transformer models Type: general – SubjectFull: Industrial equipment Type: general Titles: – TitleFull: Dynamic Anomaly Detection: A Multimodal Spatiotemporal Cross-Fusion Transformer Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Deng, Zefu – PersonEntity: Name: NameFull: Xu, Dongliang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 11 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
