A novel one‐dimensional convolutional neural network architecture for chemical process fault diagnosis
Bibliographic record
Abstract
Abstract In recent years, industrial production has become increasingly automated, with the widespread application of informational and digital technology. Fault detection and diagnosis (FDD) technology is also playing an increasingly important role in the chemical process industry. However, owing to the weak generalization ability of prior models, or prior methods not being suitable for industrial sensor signal data, the fault detection rate is not satisfactory, which is a significant limitation of many fault diagnosis methods in practical applications. In response to this problem, the one‐dimensional convolutional neural network (1D‐CNN) model can directly process signal samples without changing the one‐dimensional characteristics of the data, which may be more suitable for processing such signal data. Therefore, a new 1D‐CNN architecture is proposed for FDD. The network architectures, including convolutional layers, pooling layers, fully connected layers, and various parameters, are optimized in the proposed method. The Tennessee Eastman process (TE process) is employed to assess prominent performance of the method. To comprehensively reflect the performance of the model, three evaluation indexes are selected in this study: accuracy, F1‐score, and fault detection rate. The experimental results indicate that compared with other diagnostic methods, the 1D‐CNN model has excellent feature extraction ability, which can remarkably improve diagnostic capability in the 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".