Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network
Why this work is in the frame
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Bibliographic record
Abstract
This paper develops a methodology to combine diagnostic information from various fault detection and isolation tools to diagnose the true root cause of an abnormal event in industrial processes. Limited diagnostic information from kernel principal component analysis, other online fault detection and diagnostic tools, and process knowledge were combined through Bayesian belief network. The proposed methodology will enable an operator to diagnose the root cause of the abnormality. Further, some challenges on application of Bayesian network on process fault diagnosis such as network connection determination, estimation of conditional probabilities, and cyclic loop handling were addressed. The proposed methodology was applied to Fluid Catalytic Cracking unit and Tennessee Eastman Chemical Process. In both cases, the proposed approach showed a good capability of diagnosing the root cause of abnormal conditions.
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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 | 0.001 | 0.001 |
| 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 it