A Linguistically Interpretable Fuzzy Fault Diagnosis Model: Knowledge Distillation Perspective
Bibliographic record
Abstract
Emerging deep fuzzy neuralnetwork-based fault diagnosis (FD)—integrating deep neural networks (DNNs) and fuzzy neural networks (FNNs) in sequential or parallel architectures—has demonstrated great potential in both high performance and model interpretability. However, in the cascade architecture, the membership functions of the FNN struggle to finely describe the complex fault features extracted by the DNN, leading to reduced FD performance. Meanwhile, the abstraction of fault features undermines the FNN's interpretability. In the parallel architecture, designing a reasonable strategy to fuse the heterogeneous features extracted by the DNN and FNN is nontrivial. To address these challenges, we propose a novel FD model called linguistically interpretable fuzzy neural network with knowledge distillation, which integrates DNN and FNN through the perspective of knowledge distillation, aiming for high performance while preserving FNN's intrinsic interpretability. The model extracts modality probability knowledge from a well-trained DNN teacher unit and feeds into the knowledge distillation unit to generate soft labels encapsulating modality similarity knowledge. These soft labels carry the teacher unit's core insights into FD and activate implicit information of negative modalities. Then, the knowledge-based Takagi–Sugeno–Kang unit uses the soft labels as consequent variables to perform linguistically interpretable rule reasoning from original features to fault modalities. The model is optimized by minimizing a composite loss comprising cross-entropy, soft label regularization, and L2 regularization, ensuring more knowledge is transferred to the distilled unit. Comprehensive evaluation across a series of industrial process cases validated the model's effectiveness in performance and interpretability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".