Layer‐wise contribution‐filtered propagation for deep learning‐based fault isolation
Bibliographic record
Abstract
Abstract Deep learning is gradually mainstreaming into data‐driven methods, relying on the advantages of extracting complicated nonlinear features. However, the black‐box property makes its decision rules non‐transparent, resulting in difficulty in attribution tasks, which aim to backtrack the contribution of network inputs to the outputs. Fault isolation and localization are techniques for diagnosing the root cause of system failures, which have a consistent objective with attribution for a deep learning‐based fault observer or classifier. Unfortunately, most fault isolation methods are based on shallow learning methods. Also, many attribution algorithms are linear without considering the influence of nonlinear activation functions. The related concerns motivate us to propose a new approach, namely layer‐wise contribution‐filtered propagation (LCP), for deep learning‐based fault isolation. In LCP, reasonable contributions are defined based on the influence of each layer input on maximizing the absolute output activation. A symbolic function is designed to identify neurons with negative contributions, which are then filtered and forbidden to backpropagate to the previous layer. By guiding correct attribution, LCP is available for any nonlinear activation functions and their combinations. It also provides a solution for fault isolation with stacked sample inputs, in which one single variable has several attributions associated with different times. Finally, two chemical simulations verify the effectiveness of the proposed method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".