A transfer learning approach using improved copula subspace division for multi‐mode fault detection
Bibliographic record
Abstract
Abstract Multi‐mode characteristics of industrial processes are prominent in the area of chemical production due to a diversified market demand. Despite mounting interests in predictive modelling for the optimization of operating conditions in chemical production processes, particularly in the petrochemical industry with multiple feeds and a range of cracking furnaces, targeted solutions that could hold wider applicability are typically hindered by the lack of available data. To overcome the limitation posed by data scarcity, an inductive approach based on transfer learning for fault detection is proposed utilizing copula subspace division (CSD), named TrAdaBoost CSD (TCSD). The proposed TCSD method is based on the probability view to transfer different most similar source samples to target samples. To select the optimal number of source samples, two adaptive indices were proposed and designed to adaptively assign the optimal model training iterations and sample number per iteration. Imbalance in data samples between the target and source datasets was addressed via the adaptive active vine copula‐based probabilistic method. The effectiveness and superiority of the proposed TCSD approaches are validated via a numerical example (with the ranges of normalized fault detection and false alarm rates [FDR and FAR] between 0.82–0.94 and 0.01–0.04, respectively), the Tennessee Eastman process (~10% and 2.24% improvement for FDR and FAR, respectively), and the ethylene cracking furnace process (~15% and 8% improvement for FDR and FAR, respectively).
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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 | 0.000 |
| 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".