Simultaneous fault detection and isolation using semi‐supervised kernel nonnegative matrix factorization
Bibliographic record
Abstract
This paper presents a monitoring approach for nonlinear processes based on a new semi‐supervised kernel nonnegative matrix factorization (SKNMF). Different from the existing nonnegative matrix factorization (NMF) and kernel nonnegative matrix factorization (KNMF), SKNMF is a semi‐supervised matrix factorization algorithm, which takes advantages of both labelled and unlabelled samples to improve algorithm performance. Labelled samples refer to the samples whose memberships are already known, while unlabelled samples are a set of samples whose memberships are unknown. In fact, both NMF and KNMF are unsupervised algorithms, and they cannot make full use of labelled samples to improve algorithm performance. More importantly, we explain the reasons why labelled samples can improve algorithm performance even if the amount of labelled samples is small. Last but not least, SKNMF induces a simultaneous fault detection and isolation scheme for online processes monitoring. Case studies of a numerical example and a penicillin fermentation process (PFP) demonstrate that the proposed process monitoring approaches outperform the existing process monitoring approaches.
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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".