A modified double‐latent variable probabilistic model for monitoring of dynamic processes with multiple sampling rates
Bibliographic record
Abstract
Abstract The monitoring of quality‐correlated aspects in industrial production processes has become a crucial task in recent years. However, the challenges posed by multiple sampling rates and dynamic issues make it arduous to construct an efficient monitoring model. To address these issues, the present paper proposes a modified double‐latent variable probabilistic (MDLVP) model that can deal with the measurement correlations across different sampling rates. Firstly, the MDLVP introduces two types of latent variables with minimum sample spacing for capturing quality‐correlated and quality‐uncorrelated information respectively. Secondly, a first‐order Markov chain is utilized to describe the autocorrelation of the latent variables, thereby elucidating the dynamics of the multi‐sampling rate process. The expectation–maximization (EM) algorithm is employed for the model training in an incomplete data collection. Finally, the model is adopted to develop a fault detection method, which is subsequently applied in two industrial cases. The experimental results demonstrate the superiority of the proposed model in handling dynamic in multi‐sampling rate processes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".