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Record W4416323556 · doi:10.1109/tdei.2025.3634477

Aging Mechanism of Composite Insulated Cross-Arm Under Multi-Factor Coupling Effect

2025· article· W4416323556 on OpenAlexaff
Huijie Li, Yafeng Chao, Te Li, Fanghui Yin, Hongwei Mei, Liming Wang, M. Farzaneh

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2025
Typearticle
Language
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNational Natural Science Foundation of China
KeywordsComposite numberSilicone rubberMoistureCoupling (piping)Natural rubberHumidityStress (linguistics)Work (physics)

Abstract

fetched live from OpenAlex

Composite insulated cross-arm (CICA) can be used in high voltage transmission lines to substitute composite insulators with iron cross-arms. In addition to its excellent insulation performance, CICA can also save on transmission corridors, etc. Nevertheless, due to the horizontal arrangement, CICA bears complex mechanical stresses in addition to the high temperature and humidity environmental factors. The aging mechanism under the multifactor coupling effect is studied in this paper based on a 110 kV CICA. The test results of the interface property show that the hygrothermal factor is the main reason for aging. In addition, stress affects the interface properties by promoting moisture penetration. The interfacial current changes can be divided into three stages: the voltage-current synchronous growth stage, the voltage-continuous current plunge stage, and the interfacial breakdown stage, during the multi-factor aging process. Besides, the CICA experiments show a synergistic effect between the hygrothermal and mechanical factors in the deterioration process of the silicone rubber and the core rod. Constant force and high and low circumferential cyclic forces mainly affect the mechanical properties. This work can be helpful for the application and promotion of CICA.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.299
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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