Continual learning for fault diagnosis considering variable working conditions
Bibliographic record
Abstract
Traditional Neural Networks (NNs) trained in a one-stage process often struggle to perform well when presented with new classes or domain shifts in testing datasets. In fault diagnosis, it is essential to handle a sequence of diagnostic tasks with new fault classes and working conditions. This paper presents a multi-staged Continual Learning algorithm that learns from a sequence of diagnostic tasks. In each training stage, a small portion of previously seen training data is incorporated to help the model remember old tasks and better learn new tasks. A novel scheme is designed to select previously seen data from multiple old tasks, considering their different working conditions. A multi-way domain adaptation is then conducted to mitigate the impact of multiple changes in working conditions among different tasks. The proposed method is tested using two different experiment test rigs, including both gear and bearing faults. Results demonstrate that the proposed Continual Learning algorithm allows NNs to learn from a sequence of diagnostics tasks efficiently and maintain high accuracies for all the tasks of interest.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".