Output‐relevant fault detection and identification of chemical process based on hybrid kernel T‐PLS
Bibliographic record
Abstract
The single kernel total projection to latent structures (T‐PLS) would lead to a higher missing alarm rate and false alarm rate because either global kernel function or local kernel function can be utilized. The hybrid kernel T‐PLS algorithm proposed in this paper combines global function with local function to solve non‐linear problems by projecting low‐dimensional input data to high‐dimensional feature space. The feature space is then divided into output directly correlated subspace, output orthogonal subspace, output uncorrelated subspace, and residual subspace by output variables. Based on fault detected by statistic D and Q in these subspaces, respectively, fault‐free data are reconstructed and the fault magnitude is estimated by generalized reconstruction‐based contribution (RBC). The simulation results of Tennessee Eastman process show the proposed algorithm can not only detect output‐relevant fault with higher detection rate, but also identify the type of fault.
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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".