Zero-Sample Fault Diagnosis for Bearings Using an Hierachical Constrast Learning Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fault diagnosis in rotating machinery is critical for ensuring reliable operation and reducing maintenance costs. Bearings, being essential components, require particular attention. Traditional diagnostic methods depend on labeled training data, limiting their ability to detect previously unseen faults. This limitation has driven the development of zero-sample fault diagnosis. Existing zero-sample approaches depend on manual semantic frameworks, which require significant expert input. Automated methods, however, often create semantic redundancy, reducing accuracy. To address these challenges, this paper introduces a novel hierarchical automatic semantic construction framework based on contrastive learning (HCL). The proposed method extracts fault features using contrastive learning. The semantics are organized into a hierarchical tree structure based on the contrast measurement of fault categories. Next, two autoencoders and a semantic classifier are employed to align extracted fault features with their corresponding semantic vectors, ensuring accurate fault recognition. Experimental results on a bearing dataset evaluated the effectiveness of the method.
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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 it