Quality‐related process monitoring approach based on sparse autoencoder and comprehensive <scp>KPLS</scp>
Bibliographic record
Abstract
Abstract The partial least squares (PLS) model is widely employed in quality‐related process monitoring due to its ability to effectively establish a linear relationship between process and quality variables. To extend this capability to nonlinear scenarios, kernel partial least squares (KPLS) was introduced. However, the use of a single kernel function is often inadequate for fully capturing nonlinearity. In this paper, a novel method for quality‐related process monitoring that integrates sparse autoencoders (SAE) with two KPLS models, termed SAE‐CKPLS, is developed. The SAE is utilized to extract representative features from the process variables, after which two KPLS models are constructed to explore the relationship between these extracted features and residuals with the quality variables. Additionally, two Hotelling's monitoring statistics are derived from the decomposed subspaces to detect quality‐related faults. The capability and effectiveness of the proposed SAE‐CKPLS method are demonstrated through applications to both a hot rolling mill process and the industrial Tennessee Eastman process (TEP) benchmark, with comparative analysis against related methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".