Process Monitoring Using Domain-Adversarial Probabilistic Principal Component Analysis: A Transfer Learning Framework
Bibliographic record
Abstract
Probabilistic principal component analysis (PPCA) is a feature extraction method that has been widely used in the field of process monitoring. However, PPCA assumes that training and testing data are drawn from the same input feature space with the same distributions. This assumption is not valid for complex processes that exhibit multiple operating modes and generate data with different distributions. In this article, we propose a novel transfer learning approach to monitoring processes with data from multiple distributions. To this end, we introduce a novel extension of PPCA, which is we refer to as the domain adversarial probabilistic principal component analysis (DAPPCA). DAPPCA algorithm automatically learns feature representations that are relevant across different operational modes. The algorithm extracts the most informative shared fault features and improves the accuracy of the fault detection model in a new operating mode using the knowledge transferred from previously known modes. The parameters of DAPPCA are estimated using a variational inference approach, and the monitoring statistics are calculated using the proposed model. We demonstrate the efficacy and real-time applicability of the proposed method with simulated and industrial examples.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".