View in EDS HTML Full Text PDF Full Text

UHV Converter Transformer Equipment Fault Diagnosis via Cross-Modal Transformer with DGA and Infrared Image Fusion.

Saved in:
Bibliographic Details
Title: UHV Converter Transformer Equipment Fault Diagnosis via Cross-Modal Transformer with DGA and Infrared Image Fusion.
Authors: Yang, Xin1,2 (AUTHOR) yangxin0993@163.com, Liao, Wenlong1,2 (AUTHOR), Liu, Rui1,2 (AUTHOR), Fan, Songhai1,2 (AUTHOR), Feng, Yun1,2 (AUTHOR), Zhang, Yu1,2 (AUTHOR), Yang, Yueping1,2 (AUTHOR), Wang, Zhenyu1,2 (AUTHOR), Mu, Zhou1,2 (AUTHOR)
Source: Energies (19961073). Jun2026, Vol. 19 Issue 12, p2747. 17p.
Subject Terms: *Multisensor data fusion, *Transformer models, *Electric transformers, *Gas analysis, *Thermography, *Fault diagnosis
Abstract: Ultra-high-voltage (UHV) converter transformer equipment is critical for UHVDC transmission systems. This paper proposes a Cross-modal Transformer framework for fault diagnosis by fusing dissolved gas analysis (DGA) and infrared (IR) thermography data. The framework encodes DGA measurements into temporal tokens and processes IR images through a ResNet-18 backbone to generate spatial tokens. A Cross-modal Transformer module enables deep semantic interaction via bidirectional cross-attention, allowing DGA tokens to attend to relevant IR regions and vice versa. A modality-gating mechanism adaptively reweights the two modalities under measurement degradation, including partial and fully missing-modality scenarios. The novelty lies in adapting these components into a leakage-controlled DGA-IR diagnostic framework for UHV converter transformers, with explicit interaction between gas-evolution tokens and spatial thermal tokens. Evaluation is performed under a leakage-controlled grouped chronological split that isolates equipment units, converter stations, and fault episodes across train, validation, and test partitions. Labels are drawn exclusively from maintenance inspection and operational records, independent of the IEC 60599 ratio features seen by the model. Under this protocol, the proposed framework consistently improves accuracy and macro-F1 over encoder-matched simple-fusion baselines (Transformer-DGA + ResNet-18 with concatenation, late fusion, and gated averaging). Additional missing-modality, noise, and ablation experiments indicate that the gains come from bidirectional cross-attention and adaptive gating rather than from stronger unimodal encoders alone. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 194909196
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: UHV Converter Transformer Equipment Fault Diagnosis via Cross-Modal Transformer with DGA and Infrared Image Fusion.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Xin%22">Yang, Xin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> yangxin0993@163.com</i><br /><searchLink fieldCode="AR" term="%22Liao%2C+Wenlong%22">Liao, Wenlong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Rui%22">Liu, Rui</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fan%2C+Songhai%22">Fan, Songhai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Feng%2C+Yun%22">Feng, Yun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yu%22">Zhang, Yu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Yueping%22">Yang, Yueping</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Zhenyu%22">Wang, Zhenyu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mu%2C+Zhou%22">Mu, Zhou</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 12, p2747. 17p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br />*<searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+transformers%22">Electric transformers</searchLink><br />*<searchLink fieldCode="DE" term="%22Gas+analysis%22">Gas analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Thermography%22">Thermography</searchLink><br />*<searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Ultra-high-voltage (UHV) converter transformer equipment is critical for UHVDC transmission systems. This paper proposes a Cross-modal Transformer framework for fault diagnosis by fusing dissolved gas analysis (DGA) and infrared (IR) thermography data. The framework encodes DGA measurements into temporal tokens and processes IR images through a ResNet-18 backbone to generate spatial tokens. A Cross-modal Transformer module enables deep semantic interaction via bidirectional cross-attention, allowing DGA tokens to attend to relevant IR regions and vice versa. A modality-gating mechanism adaptively reweights the two modalities under measurement degradation, including partial and fully missing-modality scenarios. The novelty lies in adapting these components into a leakage-controlled DGA-IR diagnostic framework for UHV converter transformers, with explicit interaction between gas-evolution tokens and spatial thermal tokens. Evaluation is performed under a leakage-controlled grouped chronological split that isolates equipment units, converter stations, and fault episodes across train, validation, and test partitions. Labels are drawn exclusively from maintenance inspection and operational records, independent of the IEC 60599 ratio features seen by the model. Under this protocol, the proposed framework consistently improves accuracy and macro-F1 over encoder-matched simple-fusion baselines (Transformer-DGA + ResNet-18 with concatenation, late fusion, and gated averaging). Additional missing-modality, noise, and ablation experiments indicate that the gains come from bidirectional cross-attention and adaptive gating rather than from stronger unimodal encoders alone. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194909196
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19122747
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 2747
    Subjects:
      – SubjectFull: Multisensor data fusion
        Type: general
      – SubjectFull: Transformer models
        Type: general
      – SubjectFull: Electric transformers
        Type: general
      – SubjectFull: Gas analysis
        Type: general
      – SubjectFull: Thermography
        Type: general
      – SubjectFull: Fault diagnosis
        Type: general
    Titles:
      – TitleFull: UHV Converter Transformer Equipment Fault Diagnosis via Cross-Modal Transformer with DGA and Infrared Image Fusion.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yang, Xin
      – PersonEntity:
          Name:
            NameFull: Liao, Wenlong
      – PersonEntity:
          Name:
            NameFull: Liu, Rui
      – PersonEntity:
          Name:
            NameFull: Fan, Songhai
      – PersonEntity:
          Name:
            NameFull: Feng, Yun
      – PersonEntity:
          Name:
            NameFull: Zhang, Yu
      – PersonEntity:
          Name:
            NameFull: Yang, Yueping
      – PersonEntity:
          Name:
            NameFull: Wang, Zhenyu
      – PersonEntity:
          Name:
            NameFull: Mu, Zhou
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
          Numbering:
            – Type: volume
              Value: 19
            – Type: issue
              Value: 12
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
ResultId 1