A deep‐learning model based on <scp>MFE</scp> ‐Transformer for chemical process fault detection
Bibliographic record
Abstract
Abstract Chemical process data usually exhibits strongly nonlinear characteristics, local information loss, and sample imbalance problems. These problems make it difficult for traditional models to accurately capture the features and dependencies of chemical process data, thus affecting the fault detection effect. In order to solve the above problems, a fault detection method based on multiscale feature extraction‐Transformer (MFE‐Transformer) is proposed in this paper. First, the linear and nonlinear features in the data are captured respectively using a multiscale feature extractor. Second, local information enhancement is achieved by capturing the local information between the data by a multilayer convolutional neural network. After that, the high coupling in the data is captured using the multi‐head self‐attention mechanism in Transformer to identify the complex interactions between variables. Finally, the loss contribution of easy‐to‐classify samples is reduced by the focal loss function, while the sample imbalance problem is solved by enhancing the number of minority class samples using sliding window data enhancement. By experimental validation on penicillin fermentation (PF) process and Tennessee Eastman (TE) process datasets, the results show that the method outperforms traditional models in terms of fault detection performance.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".