A fault detection method based on sparse dynamic canonical correlation analysis
Bibliographic record
Abstract
Abstract Fault detection based on canonical correlation analysis (CCA) has received increased attention due to its efficiency in exploring the relationship between input and output. However, traditional CCA may generate redundant features in both the input and output projections while maximizing the correlations. In this paper, sparse dynamic canonical correlation analysis (SDCCA) is developed for dealing with the fault detection of dynamic processes. Through posing sparsity in the extraction of features, the interpretability of canonical variates is enhanced attributed to the sparsity of canonical weights. Based on the SDCCA model, the monitoring metric is established for fault detection. Moreover, the upper control limit (UCL) based on monitoring metrics is determined by the kernel density estimation (KDE) method to avoid the violation of the Gaussian assumption. The superiority of the proposed SDCCA‐based fault detection method is illustrated through a comparative study of the Tennessee Eastman process benchmark.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".