Adaptive process monitoring via multichannel EIV lattice filters
Bibliographic record
Abstract
Abstract Online monitoring of multivariable processes is crucial to operational safety and product quality. For this, multivariable statistical analysis methods, such as principal‐component analysis (PCA), partial least squares, and canonical variate analysis have been widely applied. However, few recursive monitoring techniques have been developed for fully dynamic and time‐varying processes. Recursive PCA has been successfully applied to monitor static time‐varying processes, but does not work for fully dynamic processes. Dynamic PCA has been developed, but its recursive variant is not available. Many processes operate in dynamic states and are often time‐varying, and the time‐varying property includes the variation of parameters and of process structure, e.g., the change of model order. A novel approach to the adaptive monitoring of multivariate dynamic and time‐varying processes by the recursive multichannel instrumental variable (IV) lattice filters was developed using the errors‐in‐variables (EIV) state space model to represent a dynamic process. To show the relationship between EIV state‐space representation of the process and a multichannel IV lattice filter, the lattice filter was used to generate a residual vector for process monitoring. By using lattice filter's ability of recursively updating the process model both in time and order, a real time, on‐line algorithm was used to update the residual vector with newly sampled process data, including a practical approach to recursive determination of the process model order. Based on the residual vector, the Hotelling T2 statistic and the associated confidence limits are used as the monitoring index. The proposed scheme was evaluated on a simulation example and a pilot plant to support the theoretical results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".