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Record W2285788648 · doi:10.4271/2005-01-3389

Reliability of the Engine Electronic Controls and a Novel Approach to Improve Service Life

2005· article· en· W2285788648 on OpenAlexaff
Sharanpal S. Sikand, Sham S. Hariram, Jayant A. Patel

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2005
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsBoeing (Canada)
Fundersnot available
KeywordsReliability (semiconductor)Reliability engineeringService lifeService (business)Computer scienceAutomotive engineeringAccelerated life testingEngineeringWeibull distribution

Abstract

fetched live from OpenAlex

Reliability of Electronic Engine Controls (EEC) for airplane jet engines is critical for reliable and economic operations of the airplane. EEC failures can cause in-flight engine shut downs and airplane dispatch delays. The economic impact of this shortcoming is very large on the airlines, engine suppliers, and airplane manufacturers. The EEC is one of the most complex and expensive components of the jet engine. Reliability must be designed into the EEC from the initial stage of design by consideration of the environment, hardware selection, manufacturing processes, software design, rigorous testing, fault detection and monitoring logic, and proper in-service trouble shooting procedures. This paper outlines an approach for designing a high reliability EEC and includes a novel design for controlling EEC cooling.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2005
Admission routes1
Has abstractyes

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