BEARING PERFORMANCE DEGRADATION ASSESSMENT BY ORTHOGONAL LOCAL PRESERVING PROJECTION AND CONTINUOUS HIDDEN MARKOV MODEL
Bibliographic record
Abstract
Bearing is the key component in rotating machine. It is important to assess the performance degradation degree of bearings for making proactive maintenance and realizing near-zero downtime. A methodology based on orthogonal local preserving projection (OLPP) and continuous hidden Markov model (CHMM) is introduced in bearing performance degradation assessment. Firstly, the time domain, frequency domain and time-frequency domain features are extracted from the vibration signals. Then, the multi-dimensional features are reduced by OLPP. And the selection of the adjacent paragraph parameters in OLPP is optimized adaptively by minimizing the ratio of between-class distance to within-class distance. A CHMM is trained by using the reduced feature in normal condition. At last, the test bearing data are input into the pre-trained CHMM to calculate the log-likelihood of the test data, which can assess the performance degradation of bearings quantitatively. A bearing accelerated life experiment is performed to validate the feasibility and validity of the proposed method.
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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 | 0.000 | 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".