Fault Detection and Classification for a Process with Multiple Production Grades
Bibliographic record
Abstract
In practice, an industrial polyethylene process produces various products, even often developing new production grades for market demand. Therefore, the process has not only multiple operating conditions, but also time-varying characteristics. In addition, the process measurements inevitably are redundant and noisy. It is a challenging problem for on-line classifying the operating conditions in the industrial process. In this paper, principal component analysis (PCA) is applied to the reference data set to reduce the dimensions of variables and eliminate the collinearities among process measurements. Since outliers are inevitable in a real plant data set, they significantly stretch the cluster centers and covariances and reach an unreliable solution. In this paper, the distance-based fuzzy c -means (DFCM) algorithm is proposed. A boundary distance for each cluster is derived for identifying outliers, which should be discarded from the reference data set. Before the on-line classification, the statistic Q and T 2 of new data have to be evaluated. If any one of the statistics is out of its control limits, it indicates the new data do not belong to the PCA subspace and they should be collected for the next model update. In this paper, the blockwise recursive formulas for updating the means and covariance matrix are derived. By utilizing the updated means and covariance, a new PCA subspace that accounts for all events is derived recursively. In addition, through rotating and shifting the coordinates of the PCA subspace, the cluster parameters on the new subspace can be directly transferred from the previous one. The proposed method was successfully applied to monitor a polyethylene process with multiple production grades and time-varying characteristics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".