Real-Time Mode Diagnosis for Processes With Multiple Operating Conditions Using Switching Conditional Random Fields
Bibliographic record
Abstract
Over the past few decades, hidden Markov models (HMMs) have been widely employed for real-time diagnosis of processes with multiple modes. Recently, the conditional random field (CRF) model has also been applied for process monitoring and proven to perform superior to the HMMs. In this paper, a new framework, termed as a switching CRF (SCRF), is designed to diagnose the modes of processes that have multiple operating conditions. In the proposed framework, multiple linear-chain CRF models are allowed to switch between each other according to a scheduling variable that reflects the real-time operating conditions. The expectation-maximization algorithm is employed for the parameter estimation of SCRF. For validation, two case studies, namely a continuous stirred tank reactor system and an experimental hybrid tank system, are employed. The results demonstrate that the proposed SCRF approach shows superior diagnosis performances to the linear-chain CRF model and the multiple HMMs for the processes with multiple operating conditions.
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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 | 0.000 | 0.000 |
| 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".