Concurrent Monitoring Strategy for Static and Dynamic Deviations Based on Selective Ensemble Learning Using Slow Feature Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Slow feature analysis (SFA) has been extensively adopted for process monitoring. Since the prominent ability of exploring dynamic information of the industrial process, SFA could monitor the process static and dynamic deviations concurrently. However, for complex and large-scale processes, it is difficult for a single SFA model to monitor the whole process well because of the complex relationship within massive volumes of variables. To address this issue and get a better monitoring performance, a novel ensemble process monitoring method based on slow feature analysis models is proposed as ensemble SFA (ESFA) in this paper. The proposed method develops a set of SFA models based on different combinations of variables, and the divisive hierarchical clustering algorithm (DHCA) is performed to pick out some models with great diversity as the base learners. Then, the fault detection results of base models would be combined into a comprehensive indicator through Bayesian inference. Furthermore, the ESFA method also provides an ES2 statistic for monitoring process dynamics to differentiate the deviations of normal operating condition changes from dynamic anomalies incurred by real faults. Finally, compared with basic SFA and several principal component analysis (PCA)-based methods, the validity of the proposed method is demonstrated through the case studies of the Tennessee Eastman (TE) benchmark process and the BSM1 process.
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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 | 0.001 |
| 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 | 0.001 |
| 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 it