Open-Set Fault Diagnosis for Industrial Rotating Machines Based on Trustworthy Deep Learning
Why this work is in the frame
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Bibliographic record
Abstract
Detecting and diagnosing faults in rotating machines is crucial for ensuring the safety and reliability of modern industrial cyber-physical systems. Traditional data-driven fault diagnosis methods have achieved significant success when dealing with a set list of known faults and working conditions. However, they become inaccurate and overconfident when faced with new fault classes outside the training set. This paper introduces a novel Evidential Abstention Classifier based on trustworthy deep learning. It can quantify prediction uncertainty and recognize new fault classes without the need for their training data. Experiment results validated the efficacy of the proposed L1 regularization in improving uncertainty quantification. They also highlighted the proficiency of the designed auxiliary training method in generating fault-discriminative features and establishing effective decision boundaries for new fault types. EAC enables accurate open-set fault diagnosis with reduced reliance on historical data, offering improved transparency in the diagnostic process.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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