Probabilistic Semantic Compression for Zero-Shot Fault Diagnosis
Bibliographic record
Abstract
Semantically bridging known and novel domains, Zero-Shot Fault Diagnosis (ZSFD) often faces two main challenges. First, deterministic semantic-fault associations fail to capture generalization. Second, automatically generated semantics are flat and redundant, impairing diagnostic inference. To address these issues, a Probabilistic Semantic Compression framework is proposed for ZSFD in this work. The approach extracts discriminative features through a contrastive projection head and constructs probabilistic semantics via a semantic projection head. The probabilistic semantics capture the uncertainty during the dynamic evolution of nonstationary fault patterns, replacing hard categorical assignments. A semantic compression module adaptively compresses the flat and redundant probabilistic semantics in a structure with both high interclass discriminability and interclass correlation. Features and semantics are mutually embedded in a shared latent space, where they are aligned based on their probabilistic distributions to enable the detection of novel faults. Extensive validation on three industrial datasets demonstrates strong generalization and effective transfer from bearings with artificially induced damage to those with naturally occurring faults.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".