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Record W3016181214 · doi:10.1109/access.2020.2980433

IEEE Access Special Section: Recent Advances in Fault Diagnosis and Fault-Tolerant Control of Aerospace Engineering Systems

2020· article· en· W3016181214 on OpenAlexaff
Bing Xiao, Hamid Reza Karimi, Xiang Yu, Qingbin Gao

Bibliographic record

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsAerospaceFault toleranceSpacecraftSpecial sectionComputer scienceComponent (thermodynamics)Systems engineeringEvent (particle physics)AutomationReliability engineeringEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

With the rapid development of automation technologies, aerospace engineering systems, including aircraft, satellite, and spacecraft, have become increasingly susceptible to system/component malfunctions. Failure to take appropriate responses to even relatively minor defects can result in highly destructive events. A conventional feedback control design may result in an unsatisfactory performance or even instability in the event of malfunctions. Because of this, fault diagnosis (FD) and fault-tolerant control (FTC) technologies that can ensure the safety of handicapped systems have attracted significant interest. FTC design and relevant techniques have provided a flexible framework for dealing with these challenges since the 1970s. There has been significant progress since the 1970s by the active research community, through symposiums and seminars, as well as the vast number of publications on the subject. The research illustrates that FD and FTC are effective and applicable in many engineering plants, especially for aerospace engineering systems. However, it still remains a challenging research area in applications relating to aircraft, spacecraft, and satellites.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.254
Teacher spread0.236 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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