Switching Conditional Random Field Approach to Process Operating Mode Diagnosis for Multi-Modal Processes
Bibliographic record
Abstract
Accurate diagnosis of process modes for industrial processes is critical to safe and reliable operation of processes. The hidden Markov models (HMMs) have been widely employed to solve the real-time process mode diagnosis problems for multi-modal processes. However, restricted by the inherent conditional independence assumptions, the process mode diagnosis performance of HMMs tends to be degraded as these assumptions can be easily broken in reality. Alternatively, the conditional random field (CRF) model has been proposed in the context of process monitoring and proven to outperform the HMMs. In this work, we extend the CRF framework to the mode diagnosis of the processes that have multiple operating conditions, by designing a new framework, termed as, a switching CRF (SCRF). In the proposed framework, multiple linear-chain CRF models are proposed which have the capability to switch between each other in accordance with a scheduling variable that is indicative of the operating conditions. The expectation-maximization algorithm is employed for parameter estimation. To validate the performance, a numerical example is employed. The results demonstrate that the proposed SCRF approach shows superior diagnosis performance to the linear-chain CRF based method.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".