A density‐based collaboration preserving projection for fault detection
Bibliographic record
Abstract
Abstract The development of effective dimensionality reduction methods for fault detection in industrial processes is essential for ensuring safety. The performance of dimensionality reduction methods depends heavily on the extraction of effective and comprehensive features. To achieve a more accurate representation of data characteristics and enhance fault detection performance, we propose a novel data dimensionality reduction method for fault detection, called density‐based collaboration preserving projection (DCPP). First, an adaptive neighbour selection strategy is proposed to dynamically select the neighbours for each sample, enabling the adjacency graph constructed using each sample and its neighbours to accurately reflect the local structure of the data. Second, DCPP effectively preserves the topological structure of the data in the spatial dimension and captures dynamic features in the temporal dimension. Third, a data cleaning method is proposed to label samples that deviate from the dominant structure, and targeted weight coefficients are constructed for these samples to enhance the reliability of DCPP in extracting features in the spatial dimension. All distance measures employ geodesic distance instead of traditional Euclidean distance, allowing for a more accurate assessment of the actual distance between two points within the manifold structure. Finally, the effectiveness of the proposed method is validated through simulation experiments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".