Quality relevant fault detection of batch process via statistical pattern and regression coefficient
Bibliographic record
Abstract
Abstract Compared with the normal process, the statistical values or the relationships between process variables and the quality that represent process situations would change in an abnormal batch. A novel method named quality relevant fault detection based on statistical pattern and regression coefficients (SPRC) is proposed for the batch process. Firstly, the statistical patterns of the process data, such as mean value and SD, are computed to quantify process characteristics. The regression model is built via linear methods, such as multiple linear regression, least absolute shrinkage and selection operator, to describe the linear relationship. The mutual information between the quality and the process parameters is used to characterize the nonlinear relationship. In this way, statistical patterns and regression coefficients which express batch information constitute the two‐way matrix. Then, the matrix is dimensionally reduced by related method, such as principal component analysis. The relationships hidden in batch process could be sought by SPRC. Finally, two cases, penicillin fermentation and steel hot rolling, are used to validate the feasibility and advantages of the proposed method.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".