Curve similarity recognition based rolling bearing degradation state estimation and lifetime prediction
Bibliographic record
Abstract
The health state of a rolling bearing keeps changing from a normal state to a slight degradation state followed eventually by a severely degraded state. To make reasonable inspection and maintenance plans, it is necessary to estimate the degradation state and predict the lifetime of a running rolling bearing accurately and in a timely fashion. This paper presents a new method for rolling bearing degradation state estimation and lifetime prediction based on curve similarity recognition. Different from existing methods, this method employs a dynamic time warping algorithm to recognize the curve similarity of those extracted features of rolling bearings in different states of health, which can reflect the intrinsic state of the rolling bearing; it discretizes the bearing degradation state reasonably through curve similarity. Next, the curve similarity is used to train the degradation state estimation model and a support vector machine based lifetime prediction model. Finally, this paper conducts a case study for a rolling bearing with impact degradation and one with wear degradation, respectively. The experimental results indicate that the new proposed method is highly efficient in recognizing the bearing’s degradation state and predicting its lifetime.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".