Rolling Bearing Early Fault Detection Method Based on Feature Clustering Fusion Degradation Index
Bibliographic record
Abstract
Abstract The research on rolling bearing early fault detection is mainly focused on degradation index extraction and adaptive setting of alarm threshold. The mainstream methods are to extract degradation indicators based on adaptive features and set adaptive alarm thresholds based on the Shewhart control chart. However, the adaptive feature extraction method does not consider the correlation between features, and the Shewhart control chart is not sensitive to small fluctuations caused by early faults. In this study, a rolling bearing early fault detection method based on a feature clustering fusion degradation index is proposed. The multidomain statistical features are extracted to form the initial feature set, and the improved hierarchical clustering algorithm is combined with the feature evaluation index to select features to form a preferred feature subset, to ensure the richness of index information and reduce redundancy. After the construction of the degradation index, to suppress the interference caused by nonstationary and abnormal shocks in early fault detection, the accurate evaluation method and anomaly determination strategy of control chart parameters are studied, and an improved exponential weighted move average control chart is designed to monitor the degradation index. The effectiveness and superiority of the proposed method are verified by public data sets. This research provides a rolling bearing early fault detection method, which can provide comprehensive degradation indicators, eliminate interference caused by random anomalies and running in periods, and achieve an accurate detection of early bearing failures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".