SF‐PFE: A slow feature extraction method based on the fusion of fast and slow pathways for parallel linear and nonlinear process monitoring
Bibliographic record
Abstract
Abstract In modern industrial processes, the behaviour of process variables often involves both linear and nonlinear dependencies, as well as distinct characteristics between high‐frequency and low‐frequency transformations. To address these complexities and improve the accuracy of process monitoring and fault detection, this research proposes a novel model called SF‐PFE, designed for parallel feature extraction and monitoring. This model combines a linear mapping module with a transformation gate to simultaneously capture both linear and nonlinear features. Inspired by the SlowFast framework, it divides time‐series data into two pathways: a slow pathway for low‐frequency data and a fast pathway for high‐frequency data. The extracted features are then integrated using methods such as feature concatenation and weighted summation, combining long‐term and short‐term data trends to enhance fault diagnosis. Furthermore, a slow feature constraint is employed to maintain variability while extracting speed‐related features, offering better insight into dynamic process behaviours. Comprehensive experiments on the Tennessee Eastman process dataset show that SF‐PFE significantly outperforms existing techniques in the literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".