Dynamic process monitoring using dynamic latent‐variable and canonical correlation analysis model
Bibliographic record
Abstract
Abstract Dynamic latent‐variable (DLV) modelling is a very effective method for dynamic process monitoring. However, the DLV method only focuses on auto‐correlation in process data but ignores the cross‐correlation between inputs and outputs. To overcome this shortcoming, a novel dynamic process monitoring method using dynamic‐latent variable and canonical correlation analysis (DLV‐CCA) is proposed. Considering the dynamics in the process data, the proposed DLV‐CCA method first utilizes the DLV method to decompose the input space into input dynamic and static subspaces. The output space is also decomposed into output dynamic and static subspaces by DLV. Then, canonical correlation analysis (CCA) is used to explore the cross‐correlation between the dynamic subspaces (including input dynamic and output dynamic subspaces) and the static subspaces (including input static and output static subspaces). According to the CCA results, residual signals are generated and corresponding Hotelling's T 2 statistics are established to detect variations in these residual signals. A numerical example and a closed‐loop continuous stirred‐tank reactor (CSTR) are employed to demonstrate the superior performance of the DLV‐CCA based process monitoring compared with other relevant methods.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".