Interpretable Industrial Soft Sensor Design Based on Informer and SHAP
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deep learning models have been widely employed in various domains, yet they have certain limitations when it comes to industrial process applications. The two main challenges are their inability to effectively handle long-sequence predictions and the complexity of their internal structure, which makes it difficult to explain the output of the model. This work aims to build accurate and interpretable soft sensors for industrial processes. The Informer model is used to build accurate soft sensors due to its proficiency in long sequences. Additionally, an interpretable machine learning algorithm, SHapley Additive exPlanations (SHAP), is used to infer the global and local contributions of each feature to the predictions. The effectiveness of the proposed algorithms is validated on real industrial fluid catalytic cracker unit data, and the results show that the Informer model has higher accuracy and better long-sequence data prediction ability. Furthermore, the SHAP analysis enhances the model’s utility by providing clear insights into the influence of individual features on the predictions, thereby increasing its transparency and trustworthiness in industrial settings.
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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 | 0.000 |
| 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 it