Fault Prognosis of Roller Bearings Using the Adaptive Auto-Step Reinforcement Learning Technique
Bibliographic record
Abstract
This paper presents the implementation of a new adaptation algorithm to model the crack propagation of roller bearings and to predict their Remaining Useful Life (RUL). The developed algorithm is designed based on the adaptive auto-step reinforcement-learning method combined with a crack propagation model. The advantage of this algorithm is that it is now able to not only estimate the defect growth rate online, but also to predict the RUL of a roller bearing element. The presented defect propagation model incorporated in this work is an extension to the Paris’s formula that is well known in the fracture mechanics community. Further, a new adaptive filtering technique, referred to as the auto-step, is presented in this paper and is used to estimate the parameters of the crack propagation model in real-time. The prognosis structure is first compares values of both the predicted and the measured defect sizes, and then, tunes the parameters of the crack propagation model. Simulation results obtained by the auto-step method are then compared with results obtained by the Recursive Least Square (RLS) adaptive filter. The proposed prognosis strategy is distinct itself from other approaches in terms of obtaining higher accuracy as well as faster convergence rate.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".