Combination of KPCA and causality analysis for root cause diagnosis of industrial process fault
Bibliographic record
Abstract
Abstract Kernel principal component analysis (KPCA) based monitoring has good fault detection capability for nonlinear process systems; however, it can only isolate variables that have a contribution to the occurrence of a fault, and thus it is not precise in diagnosing. Since there is a cause and effect relationship between different variables in a process, accordingly a network‐based causality analysis method was developed for different fault scenarios to show causal relationships between different variables and to see the causal effect between the variables most contributing to the occurrence of a fault. It was shown that KPCA in combination with causality analysis is a powerful tool for diagnosing the root cause of a fault in the process. In this paper the proposed methodology was applied to a fluid catalytic cracking (FCC) unit and the Tennessee Eastman process to diagnose root causes for different faulty scenarios.
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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".