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Record W2096546541 · doi:10.1109/icqr2mse.2011.5976594

Reliability analysis of aircraft servo-actuation systems based on the evidential networks with imprecise information

2011· article· en· W2096546541 on OpenAlexaff
Jianping Yang, Dunwei Wen, Hong‐Zhong Huang, Wan Hu, Rui Sun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsAthabasca University
Fundersnot available
KeywordsRedundancy (engineering)Reliability (semiconductor)ServomechanismComputer scienceServoControl systemControl engineeringControl theory (sociology)Fly-by-wireReliability engineeringEngineeringControl (management)SimulationFlight simulatorArtificial intelligence

Abstract

fetched live from OpenAlex

A servo-actuation system is one of the key executing subsystems of the flight control system of an aircraft. With the development of the fly-by-wire control systems, the redundant servo-actuation systems have been extensively applied. A servo-actuation system has a long life and high reliability, which results in the lack of experiment information. In the meantime, the available data is insufficient and imprecise during its product design stage. In this paper, the evidential networks or simply EN are adopted to handle the imprecise probabilities. The formulae of marginal belief mass for series and parallel systems are represented respectively. The basic reliability model and mission reliability model of a three-redundancy servo-actuation system in an aircraft flight control system are analyzed using the EN approach, respectively. The EN manage and quantify the imprecision of the servo-actuation effectively, and propagate the imprecision from the root nodes to the top nodes, which represent system reliability.

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)0.005
metaresearch head score (Gemma)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.786
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.065
GPT teacher head0.296
Teacher spread0.232 · 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 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

Citations3
Published2011
Admission routes1
Has abstractyes

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