A dual-objective contrastive learning approach with dynamic self-adaption for zero-shot fault diagnosis
Bibliographic record
Abstract
Fault type classification and fault severity identification are two critical and complementary tasks in fault diagnosis of industrial machines, providing essential information for the maintenance and safety of the machines. However, variable operating conditions in industrial settings make it hard to collect comprehensive fault data covering all possible types and severities, thereby limiting diagnostic efficiency. To overcome these challenges, a novel multi-task network approach is proposed to detect fault type and severity simultaneously even with zero novel samples. Discriminative features are extracted through a contrastive network with task-specific projection heads, enabling the capture of distinct representations for fault type and severity. Two zero-shot mapping spaces are constructed to diagnose fault types and severity by aligning feature representations with the semantic information of fault types and severity. A dynamic self-adaptation optimization mechanism is introduced considering the dependency of fault severity on fault types. It enhances the identification of fault severity. The proposed method was evaluated on two bearing datasets. It achieved up to 89.4 % accuracy for fault type and 83.42 % for fault severity under zero-shot settings, outperforming baselines and demonstrating strong real-world applicability.
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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 | 0.001 |
| 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".