A fault detection method based on horizontal visibility graph‐integrated complex networks: Application to complex chemical processes
Bibliographic record
Abstract
Abstract With the trend towards large‐scale structure and complexity in modern chemical processes, it is difficult to describe the system operating status and fault conditions via traditional approaches. In addition, there are obstacles to fault detection research in complex chemical processes due to the characteristics of the equipment hardware used in chemical systems, data collection and handling, strong internal correlations in chemical systems, factors affecting transmission, and randomness and cascade in process failures. Therefore, this paper presents a fault detection method based on horizontal visibility graph (HVG) analysis‐integrated complex networks. The data for each variable in the system are regarded as a time series, each time series is modelled into a network by the horizontal visibility algorithm, and each single‐layer network corresponding to a time series is abstracted as a node. Meanwhile, the correlation between two single‐layer networks is used to characterize the correlation between the corresponding nodes. Moreover, a complex network structure representing the chemical system can be constructed from the correlations. In addition, according to the correlation ratio matrix obtained from the fault state and the normal state, the variance of each node determines the faulty node. Finally, we verify the validity and the effectiveness of the proposed method by applying it to the Tennessee Eastman (TE) process.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".