Physics-Informed Diagnostic and Prognostic Models for Rolling Element Bearings Using Oil Debris Data
Bibliographic record
Abstract
Rolling element bearings are susceptible to rolling contact fatigue failure.This poses challenges in industrial applications like wind turbines and aircraft.To address this challenge, the thesis constructs diagnostic and prognostic models due to spalling in the inner raceway.This is achieved by integrating real-time oil debris data and a comprehensive understanding of the underlying bearing degradation mechanisms.The diagnostic model incorporates spall physical information, a heuristic "Kneedle" algorithm and a Random Forest to determine the severity of the spall propagation.For prognostics, the author thoroughly evaluates the effectiveness of the particle filter and its variants.Subsequently, the enhanced version of the Auxiliary Particle Filter with Resample Move is deployed to estimate the remaining useful life.The paramount significance of this developed model lies in its adaptability to bearings of various sizes.This versatility ensures its applicability across various industrial contexts, thus offering an effective solution. Physics-informed Diagnostic and Prognostic Models for Rolling Element Bearings Using Oil Debris Data List of TablesSummary of sensors . . . . . .
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".