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Record W2169580201 · doi:10.1109/icsmc.2007.4413703

Intelligent model-based hierarchical fault diagnosis for satellite formations

2007· article· en· W2169580201 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
FieldComputer Science
TopicAI-based Problem Solving and Planning
Canadian institutionsConcordia University
Fundersnot available
KeywordsSpacecraftFault detection and isolationFault (geology)Computer scienceSatelliteTelemetryReal-time computingSpace explorationProcess (computing)Embedded systemRemote sensingEngineeringAerospace engineeringArtificial intelligenceTelecommunicationsGeographyGeology

Abstract

fetched live from OpenAlex

Formation flying is an emerging area in the Earth and space science and technology domain that utilize multiple inexpensive spacecraft by distributing the functionalities of a single platform among the miniature inexpensive platforms. Traditional spacecraft fault diagnosis and health monitoring practices that involve around-the-clock monitoring, threshold checking, and trend analysis of a large amount of telemetry data by human experts do not scale up well for multiple space platforms. In this paper a hierarchical fault detection and isolation (FDI) framework for spacecraft formation is proposed. Furthermore, fuzzy reasoning-based fault diagnosis for formation-level fault isolation related to attitude control is investigated. The proposed method has potential for acting as a mission enhancer by automating the fault diagnosis process for satellite formation flying missions.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.805
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.036
GPT teacher head0.291
Teacher spread0.256 · 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

Quick stats

Citations14
Published2007
Admission routes1
Has abstractyes

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