Statistical Test-Based Practical Methods for Detection and Quantification of Stiction in Control Valves
Bibliographic record
Abstract
Control valve, affected by stiction, causes closed-loop signals to experience oscillations, which ultimately leads to a decrease in product quality, reduced plant throughput, and increased environmental footprint. Therefore, it is indispensable to detect and quantify stiction in control valves. To accomplish this objective, in the present work, four noninvasive practical and simple methods are developed with the help of statistical tests such as F -test, t -test (Student’s t -test), modified Hotelling T 2 -test, and reverse arrangement test (RAT). The developed methods are applied to benchmark control loops espoused from chemical, paper, and mining industries. The results of the proposed methods are compared with that of existing methods found in the literature. It is found that the t -test-based method, the modified Hotelling T 2 -test-based method, and the RAT-based method demonstrate equally good or better performance than the existing methods, while the F -test-based method outperforms some of the existing methods. In addition to detecting stiction, the proposed methods can quantify stiction to timely notify panel operators of stiction severity and assist plant maintenance engineers to arrange plant shutdowns well ahead in time. The proposed methods are applicable to all types of control loops except level loops.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".