Analysis and Modeling of Planetary Gearbox Vibration Data for Early Fault Detection
Bibliographic record
Abstract
Planetary gearboxes are key rotating motion transmission components used in many types of machinery. Syncrude Canada uses planetary gearboxes in their Fort McMurray oil sands field operations to transport stockpile for further downstream processing. There is currently no condition monitoring capability for these gearboxes. As such, unexpected failures are not detected in advance. Failure of these gearboxes results in costly bottlenecks and secondary damages. Routine inspections to check on condition of the gearbox, requiring the gearbox to be off-line, are also costly. \nThis thesis investigates into condition monitoring for Syncrude's planetary gearbox through analyzing the data collected at a test rig that is modeled after the one used in field operations. The condition at specific points in the testing is analyzed, and the desired fault to be detected early is identified. The Time Synchronous Averaging (TSA) preprocessing technique is applied to the original data, and results show that it is superior for modeling purposes. Health indicators and statistical control charts are applied based on the TSA model, and show clear indication of deterioration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".