Fatigue prediction for hoist cables over sheaves in large mining shovel application
Bibliographic record
Abstract
Abstract Cables are used in many engineering applications, whether considered as stationary or running cables. In the latter, ropes are subjected to repeated tension and bending as they run over a sheave wheel. Such loading scenarios are seen for large mining equipment such as draglines and shovel hoist ropes. Fretting fatigue failure often occurs after several cycles of loading because of wires rubbing against each other and external wires rubbing against sheave wheels. It is also pertinent to understand the behaviour of cables subjected to bending over sheaves, to be able to predict fretting fatigue life, so as to set preventive maintenance activities to avoid catastrophic failure in such systems. In this paper, the behaviour of 2 specific configuration strands, composing either 7 or 19 wires, bent over a sheave is investigated numerically. To aid preventive maintenance inspection, critical locations of stress concentrations are identified as a function of applied load or tensile stress. The investigation also considers the impact of groove size, diameter of sheave to diameter of cable ratio, and contact length, enabling the application to infer fatigue life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".