Advanced framework for intelligent fault diagnosis in rotary machinery with out-of-distribution recognition
Bibliographic record
Abstract
Abstract Fault diagnosis plays a crucial role in maintaining mechanical equipment reliability. Deep neural networks have exhibited superior performance in fault diagnosis under closed sample space assumptions. However, the deployment of neural network models in practical industrial environments faces significant challenges due to the emergence of out-of-distribution (OOD) data, particularly novel fault categories that deviate substantially from the initial training assumptions. To address these limitations, we propose a robust fault diagnosis framework incorporating OOD sample detection and adaptive learning capabilities. The framework utilizes a novel Mixture of Experts model (MoEFormer) as the feature extraction mechanism, which enables fine-grained feature representation while enhancing computational efficiency. Furthermore, we introduce a Gaussian density peak clustering algorithm for OOD data identification and autonomous model retraining. This integrated framework enables real-time adaptation and online diagnosis in industrial scenarios where novel fault categories may emerge. Experimental validation was conducted using the bearing fault dataset from the University of Ottawa and the gear fault dataset from Xi’an Jiaotong University. The results demonstrate that our proposed method outperforms existing state-of-the-art approaches in identifying unknown OOD faults and achieves superior diagnostic performance. The adaptively updated model attained a diagnostic accuracy of 96.0%, representing a significant improvement of approximately 30 percentage points compared to non-adaptive methods.
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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) | 0.000 | 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".