# Explainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks

arXiv: https://arxiv.org/abs/2412.01322 · HTML: https://mlsanity.com/paper/2412.01322/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 69 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 14 of 14)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [KAN 2.0: Kolmogorov-Arnold Networks Meet Science](https://mlsanity.com/paper/2408.10205/) (2024)
- [A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks](https://mlsanity.com/paper/2406.02917/) (2024)
- [Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2407.17611/) (2024)
- [Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems](https://mlsanity.com/paper/2406.19662/) (2024)
- [Deep Learning Alternatives of the Kolmogorov Superposition Theorem](https://mlsanity.com/paper/2410.01990/) (2024)
- [SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2411.06286/) (2024)
- [Does Your Model Think Like an Engineer? Explainable AI for Bearing Fault Detection with Deep Learning](https://mlsanity.com/paper/2310.12967/) (2023)
- [FaultNet: A Deep Convolutional Neural Network for bearing fault classification](https://mlsanity.com/paper/2010.02146/) (2020)
- [Effective Integration of KAN for Keyword Spotting](https://mlsanity.com/paper/2409.08605/) (2024)
- [KAN we improve on HEP classification tasks? Kolmogorov-Arnold Networks applied to an LHC physics example](https://mlsanity.com/paper/2408.02743/) (2024)
- [From Black Box to Clarity: AI-Powered Smart Grid Optimization with Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2408.04063/) (2024)
- [Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature](https://mlsanity.com/paper/2408.11065/) (2024)
- [Early Prediction of Natural Gas Pipeline Leaks Using the MKTCN Model](https://mlsanity.com/paper/2411.06214/) (2024)

## Built on by (most cited first, 2 of 2)

- [Comparative Evaluation of Kolmogorov-Arnold Autoencoders and Orthogonal Autoencoders for Fault Detection with Varying Training Set Sizes](https://mlsanity.com/paper/2508.02860/) (2025)
- [Parametric Generalized Adaptive Moment Features (PG-AMF) for Bearing Fault Diagnosis and Machine Health Monitoring](https://mlsanity.com/paper/2606.26317/) (2026)

## Abstract (arXiv)

Rolling element bearings are critical components of rotating machinery, with their performance directly influencing the efficiency and reliability of industrial systems. At the same time, bearing faults are a leading cause of machinery failures, often resulting in costly downtime, reduced productivity, and, in extreme cases, catastrophic damage. This study presents a methodology that utilizes Kolmogorov-Arnold Networks to address these challenges through automatic feature selection, hyperparameter tuning and interpretable fault analysis within a unified framework. By training shallow network architectures and minimizing the number of selected features, the framework produces lightweight models that deliver explainable results through feature attribution and symbolic representations of their activation functions. Validated on two widely recognized datasets for bearing fault diagnosis, the framework achieved perfect F1-Scores for fault detection and high performance in fault and severity classification tasks, including 100% F1-Scores in most cases. Notably, it demonstrated adaptability by handling diverse fault types, such as imbalance and misalignment, within the same dataset. The symbolic representations enhanced model interpretability, while feature attribution offered insights into the optimal feature types or signals for each studied task. These results highlight the framework's potential for practical applications, such as real-time machinery monitoring, and for scientific research requiring efficient and explainable models.