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Record W2113257654 · doi:10.1109/icsmc.2007.4413816

Fault detection in spacecraft attitude control system

2007· article· en· W2113257654 on OpenAlexaff
Hamed Azarnoush, K. Khorasani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)ResidualFault detection and isolationNonlinear systemObserver (physics)SpacecraftLinearizationComputer scienceReaction wheelBenchmark (surveying)Attitude controlState observerControl engineeringEngineeringControl (management)AlgorithmActuatorArtificial intelligenceAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a method for detecting faults in a reaction wheel of an attitude control system of a spacecraft. A nonlinear observer is proposed as a residual generator to detect the anomalies and faults. To justify the need for the proposed complex nonlinear observer, a linear residual generation scheme is also considered as a benchmark and also for comparison. The linear observer is designed through linearization of the highly nonlinear dynamics of the reaction wheel. The performance and limitations of the linear observer subject to the three fault scenarios commonly present in the reaction wheel are investigated. The advantages and capabilities of applying the proposed nonlinear observer are demonstrated and emphasized through numerical simulations.

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 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 score0.776
Threshold uncertainty score0.449

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 head0.004
GPT teacher head0.201
Teacher spread0.197 · 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.

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

Citations12
Published2007
Admission routes1
Has abstractyes

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