Variable moving windows based non‐Gaussian dissimilarity analysis technique for batch processes fault detection and diagnosis
Bibliographic record
Abstract
In this paper, a novel variable moving windows based non‐Gaussian dissimilarity analysis technique is developed to handle the challenges related to the batch processes such as non‐linearity, non‐Gaussianity and time‐varying dynamics. First, the independent component analysis (ICA) models are developed on the normal reference data sets and the monitored data sets to extract the dominant independent component subspaces through a variable moving windows strategy. Then, non‐Gaussian dissimilarity indices are computed to evaluate the statistical independency of the extracted IC subspaces at each specific time interval. Thus, the process non‐Gaussian features are fully captured and the process trajectories distribution information of batch‐to‐batch is quantitatively estimated for online process monitoring. Further, the non‐Gaussian contribution index based on the mutual information is introduced to identify the variables that may be responsible for the process abnormality. The reliability and validity of the proposed method are verified on the fed‐batch Penicillin Fermentation process. The application results present superior fault detection and diagnosis performance compared with the PCA dissimilarity, MPCA and MICA approaches.
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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 | 0.001 |
| 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".