An Unsupervised Knowledge and Data Dual-Driven Based Fault Diagnosis for Industrial Process
Bibliographic record
Abstract
Fault diagnosis (FD) is essential for ensuring the safety and reliability of industrial processes. While data-driven approaches have demonstrated remarkable diagnostic performance, their heavy reliance on large-scale labeled datasets poses significant challenges due to the high cost and effort required for manual annotation. Unsupervised FD methods offer a promising alternative; however, their effectiveness is often hindered by the lack of principled integration of fault-related knowledge, resulting in suboptimal interpretability and diagnostic reliability. To address this limitation, this paper proposes an unsupervised knowledge and data dual-driven (KDDD) fault diagnosis framework that embeds fault characteristics into the feature extraction process by leveraging variable associations. Specifically, we hypothesize that fault patterns manifest in the structural relationships among monitoring variables. Based on this assumption, an association graph is constructed to capture these dependencies and subsequently integrated into the feature extraction model, enabling a more informed and interpretable fault representation. Extensive experiments on a widely adopted chemical process benchmark demonstrate the effectiveness of the proposed method in enhancing fault feature extraction and improving diagnostic performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".