Multimode non‐Gaussian process monitoring based on local entropy independent component analysis
Bibliographic record
Abstract
Abstract Traditionally, independent component analysis (ICA) as a multivariate statistical process monitoring (MSPM) method has attracted considerable attention due to its excellent ability in analysis of non‐Gaussian datasets. However, it may degrade fault detection performance for multimode operating process because of its assumption of one single steady mode. In order to supervise the non‐Gaussian process with multiple steady modes more effectively, this paper proposes a process monitoring method based on local entropy independent component analysis (LEICA). This method applies local probability density estimation to remove the effects of multimode characteristics. Furthermore, information entropy theory is used to extract the feature information of process data by calculating their local information entropies. Based on these local entropy data, ICA is applied to establish the local entropy component model for fault detection. Lastly, a numerical example and the Tennessee Eastman (TE) process are used to verify the proposed method and the results demonstrate the superiority of LEICA method.
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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".