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Record W2003162108 · doi:10.1115/dscc2011-6121

Friction Fault Detection of an Electrohydrostatic Actuator

2011· article· en· W2003162108 on OpenAlexaff
S. Andrew Gadsden, Yu Song, Kevin McCullough, Saeid Habibi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsMcMaster University
Fundersnot available
KeywordsActuatorRobustness (evolution)Control theory (sociology)Kalman filterAerospaceFault detection and isolationEngineeringFilter (signal processing)Computer scienceControl engineeringArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

This article discusses the application of a novel model-based fault detection method. The method is based on the interacting multiple model (IMM) strategy, which makes use of a finite number of known operating modes. A filter is used in conjunction with the IMM in order to estimate the states and parameters of the system. The smooth variable structure filter (SVSF) is a relatively new estimation strategy, and is based on sliding mode concepts which introduces an inherent amount of robustness and stability. The combined SVSF-IMM strategy is applied on an electrohydrostatic actuator (EHA), which is a device used in the aerospace industry. Two different operating modes were created, based on varying degrees of friction acting on the EHA cylinder. The results of the friction fault detection were compared with the popular Kalman filter (KF) based IMM strategy.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.209

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 head0.013
GPT teacher head0.193
Teacher spread0.180 · 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 designBench or experimental
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

Citations2
Published2011
Admission routes1
Has abstractyes

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