Dual-Contrastive Multiview Graph Attention Network for Industrial Fault Diagnosis Under Domain and Label Shift
Bibliographic record
Abstract
Recently, domain generalization (DG) methods have been actively researched for the complex industrial fault diagnosis, which aims to learn generalized representations from historical working conditions to build a diagnosis model that can perform well on unseen working conditions. However, these methods ignore the interactions between monitoring variables, which may fail to learn the feature representation with topological structure in non-Euclidean space. In addition, these methods assume the same label distribution across historical and unseen working conditions, which is generally challenging in practice, as the probability of faults varies across different working conditions. This label shift problem can negatively impact the generalization performance. To address these issues, a novel dual-contrastive multiview graph attention network (DMGAT) is proposed in this article. Specifically, a multiview graph attention network (GAT) is designed to explore the intrinsic topological structure of the data, which learns an optimal graph structure that best serves DG by integrating both graph learning and graph convolution in a unified network architecture. In addition, a novel dual-weighted contrastive learning strategy is developed. The intradomain contrastive learning facilitates the extraction of expressive node features, while interdomain contrastive learning simultaneously considers the alignment and separation of semantic probability distributions to extract shared feature representations for multiple source domains under both domain and label shifts. Furthermore, a label sampling probability is used to weight the interdomain contrastive loss and the source domain classification loss, to encourage the model to learn from minor classes in fault diagnosis. Experiments on two cases demonstrate the superiority of the proposed method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".