Robust chemical process monitoring based on CDC‐MVT‐PCA eliminating outliers and optimally selecting principal component
Bibliographic record
Abstract
Abstract In chemical process monitoring based on principal component analysis (PCA), sampling data with outliers and optimally select principal components are two challenging problems that have a main effect on monitoring performance. Given this situation, firstly, a novel outlier detection method, i.e., a robust CDC‐MVT‐PCA method (CMP), which integrates CDC‐MVT (the closest distance to centre and multivariate trimming) with PCA to identify and eliminate the outliers, is proposed to clean sample data. Secondly, based on the cleaning sample data, PCA is employed to obtain PCs. The cumulative frequency representing the variability of each PC is defined to find the optimal PCs, which are able to represent the current variability information. Finally, selecting optimal PCs online based on the cumulative frequency of each PC (CF‐PCA) is proposed to keep the most responsive components and, thus, to improve the monitoring performance. The effectiveness of the proposed robust fault monitoring algorithm is verified through a simple numerical simulation and the Tennessee Eastman process.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".