Auxiliary codes for fault prognosis of Tennessee Eastman process using a hybrid model (CPL1.0)
Bibliographic record
Abstract
CPL1.0 is a Matlab code which can generate fault predictions of Tennessee Eastman (TE) process, based on the open-source toolbox developed by Kevin Murphy in 2005. It facilitates the calculation of Prior Probabilities (PP), Conditional Probabilities (CP), and Likelihood Evidence (LE). These are essential features required for fault prognosis purpose using Hidden Markov Model (HMM) and Bayesian Network (BN) hybrid model. Determination of the CP, PP, and LE is the most time-consuming component in the aforementioned process. The proposed code has the potential to drastically reduce the repetitive computation time thus enabling the researcher to focus on the main goal-oriented outcome. CPL1.0 is implemented as a facilitator to communicate between BN and the HMM in a hybrid fault prediction and prognosis system. The hybrid system can predict ten out of ten selected faults and can accurately prognose eight out of the ten faults.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.010 |
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".