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Record W1556526069 · doi:10.1109/aim.1997.653015

Online state and parameter estimation of an electrohydraulic valve for intelligent monitoring

2002· article· en· W1556526069 on OpenAlexaff
M. Khoshzaban, Farrokh Sassani, P.D. Lawrence

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBody orificeKalman filterNonlinear systemControl theory (sociology)Computer scienceFault (geology)Displacement (psychology)Process (computing)Measure (data warehouse)Fault detection and isolationFunction (biology)State variableFlow (mathematics)Control engineeringEngineeringArtificial intelligenceMathematicsMechanical engineeringData mining

Abstract

fetched live from OpenAlex

Summary form only given. A novel nonlinear state-space model for a two-stage proportional directional servovalve is proposed, that has all physical states and coefficients necessary for an online model-based condition monitoring and fault diagnosis algorithm which tracks abrupt or gradual changes in a number of key parameters and states. Extended Kalman filtering has enabled us to simultaneously reconstruct the system "hard-to-measure" states and estimate physical coefficients using only a few basic measurements. One of those crucial states is the instantaneous amount of fluid passing through a variable orifice valve. Using a simulation example, the flow rate has been accurately predicted through a novel model that lets the orifice effective area be an initially unknown function of the spool displacement. The area and also the size of spool deadband, both embedded in the function, are then automatically revealed during the online estimation process, while the switching between the open-to-supply and open-to-tank port models is internally made.

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: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.301

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 head0.029
GPT teacher head0.260
Teacher spread0.231 · 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

Citations11
Published2002
Admission routes1
Has abstractyes

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