A novel quality‐related process monitoring method for multi‐unit industrial processes under incomplete data conditions
Bibliographic record
Abstract
Abstract Modern industrial processes usually consist of multiple local units. With the wide application of distributed control systems, a large amount of industrial data is collected. During data acquisition and transmission, outliers and missing data commonly exist in different local units. Under such incomplete data conditions, the detection performance of traditional quality‐related process monitoring methods may be seriously degraded. This study develops a novel quality‐related process monitoring method for multi‐unit industrial processes. Considering the existence of outliers and missing data, incomplete data preprocessing is first performed on raw process data and quality data. Then, process variables are divided into several blocks according to the process knowledge. Afterwards, the quality‐related features extracted by variational information bottleneck (VIB) are used to train deep support vector data description (DSVDD). Finally, a quality‐related process monitoring strategy is designed at both block and global levels. Experimental results on the revised Tennessee Eastman process demonstrate that the proposed method has better monitoring performance under incomplete data conditions than other state‐of‐the‐art methods.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".