A portable, multiprocess, track‐based robot for in situ work on hydropower equipment
Bibliographic record
Abstract
Abstract In the hydropower industry, in situ maintenance work of turbine runners to address issues such as cavitation damage and cracking is mainly performed manually. Alternatively, the entire turbine requires disassembly and is repaired off site at greater cost. This paper presents the development and fundamentals of robotic technology designed to perform work in situ on hydroelectric equipment. A second paper surveys field implementations carried out with the technology over the past 15 years. A new portable manipulator was designed with unique track‐based kinematics well suited to accessing turbine blades in a confined space. The robot is driven by position‐controlled stepper motors but relies on a hybrid force/position controller to perform processes in contact with the work piece, such as grinding. A major obstacle for robotic repair is excessive programming time. As most work is done on curved surfaces, the robot relies on a model of curvilinear space for trajectory generation. The robot is coupled to an accurate measurement system to scan surface topography in three dimensions. It has been equipped to perform several processes, such as welding and grinding, to facilitate the manufacture and maintenance of hydropower equipment. Despite the robot's inaccuracy and flexibility, surface profiles may be reconstructed with great accuracy through the use of a controlled metal removal rate strategy that relies on an innovative dynamic model of the grinding process. © 2011 Wiley Periodicals, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".