<scp>SSAE‐KPLS</scp> : A quality‐related process monitoring via integrating stacked sparse autoencoder with kernel partial least squares
Bibliographic record
Abstract
Abstract Kernel partial least squares (KPLS) is widely employed to address the issue of nonlinearity inherent in complex industrial processes. However, KPLS can only extract shallow features from process measurements. This paper proposes a new quality‐related process monitoring method via integrating stacked sparse autoencoder (SSAE) with KPLS (SSAE‐KPLS). First, an SSAE model is employed to exploit the nonlinearity within process variables. Through SSAE, hierarchical features are learned to extract latent representations of process variables from multiple sparse autoencoder (SAE) layers. Second, the learned hierarchical features from SSAE are used as input, and the final quality variables are used as output. A KPLS model is then built to exploit the nonlinear relationship between the hierarchical features extracted from process variables and the final product quality for process monitoring. Third, Hotelling's and statistics are employed to detect the quality‐unrelated and quality‐related faults, respectively. Finally, experiments on a numerical example and the commonly used industrial benchmark of the Tennessee Eastman process (TEP) are conducted to illustrate the efficacy and merits of the proposed SSAE‐KPLS based quality‐related process monitoring method by comparing it with other 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".