Mixtures of Probabilistic PCA With Common Structure Latent Bases for Process Monitoring
Why this work is in the frame
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Bibliographic record
Abstract
In this brief, we propose the mixtures of probabilistic principal component analyzers with latent bases having a common structure for modeling and monitoring multimodal processes. The proposed modeling framework attributes a joint distribution to each element of the latent bases across all the analyzers for bringing a consistent structure for the local models that correspond to various operating modes. Hierarchical prior distributions are attributed to regularize the parameters for obtaining sparse model structures. We employ the variational Bayesian expectation-maximization algorithm to train the model from the observed data. Faults are detected online if nonconformity of a data point to the developed model is identified. Furthermore, we identify faulty latent variables, and the process variables, which are significantly contributing to the faulty latent variables, are isolated by exploiting the unique structure of the model. We illustrate our proposed approach based on the simulations conducted on the Tennessee Eastman benchmark process.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it