Methods and means of increasing operation efficiency of the fleet of electric motors in non-ferrous metallurgy
Bibliographic record
Abstract
Arranging efficient operation of the fleet of induction motors (IM) in non-ferrous metallurgy is a large-scale technical and economic problem. In scientific aspect, the problem is being solved in the framework of two research lines: in developing criteria for the efficient operation of the branch IM fleet and towards the development of methods and tools for implementing the IM fleet efficient operation. The article presents the results of the authors’ work in the mentiond areas. The basis for developing criteria for efficient operation is modeling of current operational states, taking into account the IM operational aging processes. The existing methods and models are poorly focused on fixation of the changes caused by operational aging. There exists a demand for special methods and tools for modeling the IM operational conditions. A mathematical model based on Kolmogorov equations is one of these tools. The system graph and equations of the mathematical model are given. An example of a practical calculation of the no-failure operation probabilities at different rates of repair operations is given. It is stated that the offered mathematical model can serve as an instrument for developing criteria of the IM pool efficient operation. The system of periodic operational diagnostics is ment to be a key element in enhancement of the IM fleet operation efficiency. A topological method worked out for the problems of operational diagnostics is focused upon analyzing the dynamics of operational changes taking place in the IM vector space. The matrix of current deviations is a medium of objective and reliable information about the current IM technical condition. Matching the matrices of current and limiting deviations allows us to make several essential conclusions concerning the IM technical state. The reported study was funded under as a part of state assignment (project number, FSWF-2020–0019), as well as at the expense of RFBR (project number, 20-01-00283).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".