Personalized Federated Learning with Kolmogorov-Arnold Enhanced CNN for Rotating Equipment Fault Diagnosis
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
Fault diagnosis of rotating equipment plays a crucial role in ensuring operational reliability and minimizing downtime in industrial systems. This study proposes a novel approach that integrates personalized federated learning (PFL) with a Kolmogorov-Arnold network (KAN)-enhanced convolutional neural network (CNN) to improve diagnostic accuracy across heterogeneous operational environments. The framework is designed to address variations in equipment conditions and data distributions, providing personalized models tailored to specific clients. A comprehensive evaluation is conducted using benchmark datasets, demonstrating significant improvements in fault classification accuracy, model interpretability, and training efficiency under non-IID data settings using the KAN-enhanced CNN model. This study contributes to advancing predictive maintenance practices, paving the way for resilient and intelligent industrial systems.
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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