A novel fault diagnosis framework empowered by <scp>LSTM</scp> and attention: A case study on the Tennessee Eastman process
Bibliographic record
Abstract
Abstract In the era of Industry 4.0, substantial research has been devoted to the field of fault detection and diagnosis (FDD), which plays a critical role in preventive maintenance of large chemical processes. However, the existing studies are primarily focused on few‐shot samples of process data and without considering the role of activation functions in temporal diagnostic tasks. In this paper, an end‐to‐end chemical fault diagnosis framework that combines bidirectional long short‐term memory (LSTM) with attention mechanism is proposed. In the preprocessing stage, a special sliding time window function is developed to integrate multivariate samples containing complex temporal information via operation such as subset extraction. Afterwards, the bidirectional LSTM is constructed to address dynamic and temporal relationship on longer series observation, and the attention mechanism is adopted to highlight key fault features by assigning different attention weights. A case application is performed on the enriched Tennessee Eastman process (TEP), which reduces the bias between sample statistics and larger population parameters compared to existing few‐shot sample studies. The metric evaluation experiments for six activations show that the model configured with tanh function can achieve the optimal tradeoff in chemical process tasks, providing a strong benchmark for subsequent fault diagnosis research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".