A multi‐block <scp>NMF</scp> model for <scp>non‐Gaussian</scp> process monitoring based on the adaptive partition non‐negative matrix factorization and <scp>B</scp>ayesian inference
Bibliographic record
Abstract
Abstract Non‐negative matrix factorization (NMF) is a novel technique for dimension‐reduction, which can be used to process data of non‐Gaussian and Gaussian efficiently. A global NMF model is inappropriate for the whole process, since it neglects the local information and monitoring results are often hard to be interpreted. On the basis of adaptive partition non‐negative matrix factorization (APNMF) and Bayesian inference, a multi‐block NMF model for non‐Gaussian process monitoring is put forward to detect and isolate the faults effectively. Using APNMF method, the original variables in different fault states can be adaptively divided into multiple sub‐blocks, and on this basis, the NMF monitoring model of each sub‐block is formed. Then, two new statistics are constructed by Bayesian inference to supply an intuitive display. Finally, a weighted reconstruction‐based contribution (RBC) plot method is presented to reduce the smearing effect and find out the main causes of these faults. This method makes full use of the local and global information of process data and improves the effectiveness of process monitoring. The validity and feasibility of the proposed method will be proved by an example of a numerical process, a Tennessee Eastman (TE) benchmark process and a continuous stirred‐tank reactor (CSTR) process.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".