A parameter based transfer learning fault diagnosis method under different working conditions
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
In industrial applications, equipment is often worked under multiple working conditions, making it prone to various failures. Because of the lack of enough training data, the use of data-driven fault diagnosis methods is often restricted. In this paper, to address such a problem, a parameter based transfer learning(TL) method for few-shot fault diagnosis under different working conditions is proposed. In the methodology, Marginal Distribution Adaptation (MDA) is first used to decrease the divergence via minimizing the maximum mean distance between two marginal distributions of target and source domain. Then, the fault diagnosis model is built by training the from new target domain dataset and new source domain dataset. Finally, part of new target domain dataset are used to test the diagnosis accuracy of the model. Experimental results on bearing benchmark data sets from the University of Ottawa validate the proposed method. Compared with other effective fault diagnosis approaches reported in the literature, the proposed method can obtain higher diagnosis recognition rates and stronger robustness.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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