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Record W2049797486 · doi:10.1109/carpi.2010.5624460

In-situ robotic interventions in hydraulic turbines

2010· article· en· W2049797486 on OpenAlexaffabout
Bruce Hazel, Jean Côté, Y. Laroche, P. Mongenot

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsRobotTrajectoryTurbineMarine engineeringComputer scienceKinematicsMechanical engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents the development and implementation of a robotic technology designed to perform in-situ interventions in hydroelectric turbines. A new manipulator was designed with a unique, track-based kinematics well suited to access turbine blades in a confined space. As most work is done on curved surfaces, the robot relies on a curvilinear space model for trajectory generation. Several processes such as gouging, welding, grinding and hammer-peening have been integrated into the robot to facilitate the maintenance of turbines. The robots have been extensively employed by Hydro-Québec (HQ) for cavitation and crack repairs in its turbines. Recently, the robots were used to perform interventions in turbines based on fluid flow numerical analysis. For these new applications, a technology capable of reshaping the surface's profile with high precision was developed. More than 30 successful field interventions involving up to three robots working simultaneously have been performed in HQ turbines over the last 15 years.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.236
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2010
Admission routes2
Has abstractyes

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