Constant component separation for nonlinear time‐varying process monitoring based on stationary subspace analysis and one‐class <scp>SVM</scp>
Bibliographic record
Abstract
Abstract Chemical processes often exhibit non‐stationary characteristics, making traditional multivariate statistical methods unsuitable. Stationary subspace analysis (SSA) is an effective approach for handling non‐stationary variables by decomposing multivariate non‐stationary time series to identify stationary components. Additionally, the complexity of modern industrial processes introduces nonlinear relationships among variables. One‐class support vector machine (OCSVM) uses kernel tricks to map low‐dimensional nonlinear data into high‐dimensional space for linear separability and employs hyperplanes in high‐dimensional space for anomaly detection. Industrial data typically possess both non‐stationary and nonlinear characteristics; however, most existing methods, including SSA and OCSVM, address only one aspect, failing to comprehensively extract features and resulting in suboptimal monitoring performance. This study proposes an SSA‐OCSVM collaborative monitoring strategy for non‐stationary processes. By extracting stationary components with SSA and capturing nonlinear features with OCSVM, a monitoring statistic (MS) is constructed to enhance process monitoring. The proposed strategy effectively integrates the synergistic effects of both models on monitoring performance. Two case studies were conducted to validate the strategy and compare it with other methods. The results show that this method can comprehensively extract non‐stationary and nonlinear features, making it suitable for monitoring non‐stationary processes. Moreover, the monitoring results outperform those of single‐objective methods, enhancing the robustness and accuracy of the monitoring performance.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".