Fault diagnosis of chemical processes considering fault frequency via Bayesian network
Bibliographic record
Abstract
ABSTRACT In the present study, data‐driven fault diagnosis (FD) systems of chemical plants dealing with frequent and rare faults are investigated. Although different faults occur with different frequencies in chemical plants, this issue has scarcely been addressed in developing a process FD system. A novel diagnostic framework based on the Bayesian network (BN) is proposed to incorporate fault frequencies. This probabilistic method can readily involve non‐uniform probability distribution of faults and non‐Gaussian probability distribution of features. The proposed approach includes not only a combination of tools but also information management. In fact, the imbalanced dataset, established by frequent and rare faults, promotes recursive updating of prior probabilities of faults. The performance of the BN was evaluated and compared with the conventional C4.5 method in the Tennessee‐Eastman process benchmark. It was shown in this work that the diagnostic performance of the proposed approach versus the C4.5 method is more efficient. The importance of taking into account non‐uniform probability distribution of faults for designing a FD system was highlighted. Furthermore, the effect of independent component analysis (ICA) of the imbalanced dataset on the FD was examined. The proposed framework versus the C4.5 method promises 37 % FD performance improvement for the dataset with a 10:1 imbalance index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".