Dynamic process monitoring using multiscale PCA
Bibliographic record
Abstract
Conventional principal component analysis (PCA) is ideally suited for monitoring steady state processes based on the assumption that the measurements are time independent (uncorrelated) and normally distributed. Typically, most of the processes are in dynamic state, with various events occurring such as abrupt process changes, low drifts, bad measurements due to sensor failures, human errors, etc. Data from these processes are not only cross-correlated, but also auto-correlated. Applying conventional PCA directly to dynamic systems can raise false alarms, making it insensitive to detect and discriminate different kinds of events. Every event is associated with a certain frequency band according to its power spectrum. Wavelets are emerging tools to decompose a signal into various frequency bands providing simultaneous time-frequency domain analysis. We combine the potential of wavelets with the congeniality of PCA to monitor dynamic multivariate processes at different scales (frequencies). This multiscale monitoring strategy extends the suitability of PCA to statistically monitor processes based on auto-correlated measurements. Additionally, the resulting PCA models are more sensitive in detecting changes in a process. These ideas are illustrated by a suitable example.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".