Degradation Tracking of Rolling Bearings Based on Local Polynomial Phase Space Warping
Bibliographic record
Abstract
The condition monitoring of rolling bearings has received much attention in prognostics and health management. Real-time monitoring of the bearings’ degradation provides vital information for planned maintenance of machinery. However, tracking this degradation is challenging due to the hidden nature of the damages. In this article, the local polynomial phase space warping (LPPSW) algorithm is proposed to monitor the damages of bearings with high accuracy. Damages change the parameters of bearing dynamical systems and warp the trajectory in reconstructed phase space (PS). In the LPPSW algorithm, the kernel function is applied to weigh the local nearest neighbor points in the reconstructed PS. Meanwhile, the quadratic polynomial model is designed to predict the reference PS trajectory. The trajectory error between the reference PS and the damaged PS is then computed by the LPPSW. Finally, the degradation is tracked in real time. Numerical simulations and run-to-failure experiments of bearings are employed to demonstrate the effectiveness of the LPPSW. The experimental results demonstrate that the LPPSW reveals a more obvious degradation trend when compared with PS warping method and commonly used damage indicators. The proposed LPPSW algorithm improves damage monitoring capabilities while boosting the predictive maintenance of bearings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".