Dynamic industrial process monitoring based on concurrent fast and slow‐time‐varying feature analytics
Bibliographic record
Abstract
Abstract Dynamic features always demonstrate the coexistences of local fast‐variabilities and the overall slow‐trend. Single model based methods cannot achieve a satisfactory monitoring result in industrial processes with composite dynamicities. A comprehensive dynamic process monitoring method based on concurrent fast and slow‐time‐varying feature analytics is proposed in this paper. A sequential correlation coefficient is defined firstly, and all the process variables are divided into fast and slow‐time‐varying blocks. Afterwards, dynamic inner principal component analysis (DiPCA) and slow feature analysis (SFA) are employed to establish monitoring models in both blocks, respectively. Based on Bayesian inference mechanism, a novel weighted fusion strategy is designed. Finally, the proposed method is verified by the Tennessee Eastman process. With the integration of the monitoring results in both blocks, the performance of the proposed method is superior to the traditional ones.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".