Two‐step support vector data description for dynamic, non‐linear, and non‐Gaussian processes monitoring
Bibliographic record
Abstract
Abstract In this study, a the two‐step support vector data description (TS‐SVDD) method is proposed to handle the problem of fault detection for dynamic, non‐linear, and non‐Gaussian processes. First, the dynamic structure of the data is identified and the data is divided into two components: innovation component and dynamic component. Then, the innovation component is used to make the SVDD model for fault detection. Moreover, in order to overcome the issue with two‐step principal component analysis (TS‐PCA) that the choice of parameters q and D affects the fault detection effect of the methods, a genetic algorithm (GA) is used to optimize the parameters. The proposed method combines the advantages of TS‐PCA in processing dynamic process data and SVDD in dealing with non‐linear and non‐Gaussian process data. In order to evaluate the effectiveness and superiority of the proposed method, TS‐SVDD is applied to the Tennessee Eastman (TE) process and the intelligent industrial processes control test facility (I2PC‐TF), and the fault detection performance is compared with TS‐PCA and SVDD in terms of dault detection rate (FDR) and false alarm rate (FAR). The results show that TS‐SVDD has a better monitoring performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".