Control loop performance monitoring based on weighted permutation entropy and control charts
Bibliographic record
Abstract
ABSTRACT The simple calculated permutation entropy is an intuitive index to measure the complexity of a time series by a comparison of neighbouring values, therefore, it can be used to detect dynamical changes in a time series. However, in permutation entropy, the amplitude information of a time series is ignored. By incorporating the variance information, weighted permutation entropy (WPE) can derive stable results, and enable the detection of abrupt changes in a time series. In this paper, weighted permutation entropy is employed to build the performance index from the closed loop output time series to monitor the control loop performance. Furthermore, two control charts are established to define the control limits for sample estimations of the WPE‐based performance index. In this study, the Shewhart control chart and the exponentially weighted moving average (EWMA) control chart are integrated to develop two control performance monitoring schemes, Shewhart‐WPE and EWMA‐WPE. In addition, a numerical simulation is used to illustrate the ability and effectiveness of the developed Shewhart‐WPE scheme. The proposed EWMA‐WPE scheme is applied to monitor a natural gas pipeline transportation pressure control loop. The effectiveness of the proposed Shewhart‐WPE and EWMA‐WPE schemes is verified by the results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".