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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".