Robust Variational Bayesian-Based Soft Sensor Model for LPV Processes With Delayed and Integrated Output Measurements
Bibliographic record
Abstract
To satisfy the objectives of industrial process control and automation, accurate real-time measurements of quality variables are desired. While most of the process variables are measured frequently, some quality variables cannot be recorded regularly due to economical considerations or technical limitations. To measure the quality variables of some processes, samples are usually collected over a considerable time interval (integration interval) and sent to the laboratory. Due to the time-consuming offline analysis in the laboratory, the measurements would be available only after a significant delay. The lack of frequent measurements for such variables may hamper the performance of control and optimization techniques. Furthermore, the processes often show time-varying properties due to operating over different conditions, aging, and etc. This paper proposes a soft sensor model for the quality variables in linear parameter varying (LPV) processes subject to unknown varying integration intervals, unknown varying delays, and outliers. The unknown parameters of the soft sensor model and noise variance along with their uncertainties are estimated using a robust variational Bayesian algorithm. Also, the proposed algorithm estimates various statistics based on a nonparametric distribution technique. Finally, a numerical example and an experimental study on a hybrid three-tank system demonstrate the advantages of the developed model.
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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".