Semi-Supervised Probabilistic Predictable Feature Analysis for Concurrent Process-Quality Monitoring of a Thermal Power Plant
Bibliographic record
Abstract
With the advent of statistical concurrent process-quality monitoring methods, quality-relevant faults, and quality-irrelevant faults can be simultaneously discovered. However, in the presence of incomplete and asynchronous measurements of quality variables, corresponding dynamic process monitoring has become a challenging task. To address such issues, we propose a probabilistic method, termed semi-supervised probabilistic predictable feature analysis (SSPPFA), for online quality-relevant process monitoring. When modeling, both easy-measured auxiliary variables and available quality variables are leveraged to form a concurrent state-space framework. Correspondingly, the improved expectation-maximization (EM) algorithm is designed to solve the parameter estimation problem, in which the Kalman smoothing method can be flexibly adaptable to the missing issues and multirate sampling problems of quality variables. Furthermore, four statistical indices, namely$T_{x,y}^{2}$, squared prediction errors$\mathrm {SPE}_{\mathit {x}}$and$\mathrm {SPE}_{\mathit {y}}$, and the dynamic index$\mathrm {DI}_{x,y}$, are designed to realize the abnormal condition detection. The advantage of the proposed SSPPFA-based monitoring framework is demonstrated through two practical faults of a medium-speed coal in a thermal power plant.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".