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Record W3013907496 · doi:10.1109/eic43217.2019.9046586

Investigation of Partial Discharge in Aircraft Conformally-Coated Printed Circuit Boards

2019· article· en· W3013907496 on OpenAlexaff
Ehsan Zeynali, Ryan Bridges, Behzad Kordi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPartial dischargePrinted circuit boardElectrical conductorMaterials scienceCoatingElectrical engineeringVoltageAerospaceHigh voltageAutomotive engineeringReliability (semiconductor)Insulation systemComposite materialPower (physics)EngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Printed circuit boards (PCB) are key components of any electronic system. The reliability of PCBs under operating conditions is an important factor to ensure the performance of the system. In recent years, the electrical power demand in more-electric aircraft has significantly increased. To provide more power, the operating voltage is increased which imposes a higher level of electrical stress on the insulation system of PCBs and, therefore, a higher risk of failure. Partial discharges in PCBs are more likely to happen within the air gap. To improve the insulation of PCBs, they are coated with an insulation material. In this study, test boards with two parallel traces were fabricated and coated based on aerospace industry approved standards. The boards were energized using a 60-Hz adjustable high voltage source. Partial discharges were measured for silicone-coated test boards under pollution and conductive particles conditions. The impact of pollution location on partial discharge was investigated. Besides, the breakdown voltage of test boards was measured under different coating conditions and low air pressure. An increased withstand voltage for coated boards was confirmed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.233
Teacher spread0.214 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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