A propagation path-based interpretable neural network model for fault detection and diagnosis in chemical process systems
Bibliographic record
Abstract
Process monitoring through automated fault detection and diagnosis (FDD) plays a crucial role in maintaining a productive and reliable chemical process system. Developments in AI and machine learning have boosted FDD model performances especially with deep learning methods. However, these neural network models are considered black-boxes where the reasoning behind a diagnosis is unclear, preventing industrial adoption. Therefore, in this study, an interpretable neural network model is proposed for FDD in chemical processes. This framework detects and diagnoses faults based on the propagation paths of different faults which are embedded into the architecture through graph convolutional networks. A mechanism for interpreting the node activations which represent process variables is developed for decision verification. The proposed method is evaluated on the benchmark Tennessee Eastman Process where it achieves a 93.56% accuracy on selected faults. • Interpretable neural network for FDD using process data and fault propagation path. • Mechanism for interpreting node activations for model decision validation. • Sensitivity analysis and tuning guidelines for hyperparameters are provided.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".