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Record W2145880130 · doi:10.1109/icma.2011.5986282

Aircraft wiring fault evaluation based on modeling and parameter identification

2011· article· en· W2145880130 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical Fault Detection and Protection
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIdentifiabilityFault (geology)Identification (biology)Computer sciencePoint (geometry)Equivalent circuitElectronic circuitStuck-at faultElectronic engineeringFault modelAvionicsFault detection and isolationEngineeringReliability engineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Wiring problems have been a critical safety issue with aging aircraft. Most existing wiring diagnostic methods are implemented via additional devices such as transmitters, receivers or dedicated sensors, which may hold back their practicability under certain circumstances. In this paper, attempts are made from the point of view of circuit modeling and parameter identification to evaluate wiring faults with signals produced by the circuit involving the faulty wire itself. Wires of the load circuit are modeled as cascades of T-type equivalent circuits, and deviations of the load system parameters may reflect wiring faults. Circuit model with consideration of single wiring fault is developed, and the identifiability of the parameters related to the fault is analyzed. Simulations are performed to evaluate the effectiveness of the proposed method on parallel fault detection, and the results are presented with in-depth discussions.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score0.296

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)0.0000.000

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 head0.045
GPT teacher head0.256
Teacher spread0.210 · 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