Bayesian Control Loop Diagnosis by Combining Historical Data and Process Knowledge of Fault Signatures
Bibliographic record
Abstract
Many performance monitoring algorithms (or monitors) have been developed to assess control performance and detect problems with specific components; however, these algorithms monitor single components as stand-alone experts and can be influenced by other problems that they were not meant to detect. Thus, the occurrence of a problem can lead to flood of abnormal monitor outputs and alarms which can be difficult to interpret. This work focuses on how to combine information from the many different monitoring algorithms and some of process knowledge in order to obtain a more reliable diagnosis. While traditional statistical or data-based methods need data from all abnormal cases that they should diagnose/isolate, this work focuses on how to improve the Bayesian control loop diagnosis by integrating process knowledge and training data when some of the abnormality data are sparse or not available in historical database. Simulation of the proposed Bayesian diagnostic system on the Tennessee Eastman challenge problem is presented. It is demonstrated that the diagnosis is possible even when there are no training data (or only few samples) from some abnormalities.
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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 | 0.001 |
| 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".