Quality-Related Fault Detection for Processes with Time-Varying Measurements
Bibliographic record
Abstract
Partial least-squares techniques have been widely used for quality-related fault detection purposes when there is a linear relation between process variables and quality-outputs. However, those methods are suitable when the process variables and quality-outputs have constant mean values. In this paper, a novel least-squares-based scheme is proposed for quality-related fault detection of the dynamic linear processes, in which the measured variables and quality-outputs may have time-variant mean values. The proposed method is built upon a cascade modeling framework including complete orthogonal projection of the process variables onto quality-related and quality-unrelated subspaces and finding the principal manifolds for the projected components which represent their underlying auto-regressive moving average models. Moreover, a new residual index is proposed, which is insensitive to the mean changes of the process variables. The proposed method and dynamic improved partial least-squares (DIPLS [1]) technique are finally applied to a numerical case study and a non-isothermal continuous stirred tanks reactor (CSTR) benchmark, and the simulation results demonstrate the effectiveness of the proposed scheme.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".