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

TL;DR

This paper introduces a novel, interpretable machine learning framework using Kolmogorov-Arnold Networks for fault detection and severity classification in rolling element bearings, achieving high accuracy and explainability.

Contribution

The study presents a unified, lightweight, and explainable fault diagnosis framework that combines automatic feature selection, hyperparameter tuning, and symbolic interpretability using Kolmogorov-Arnold Networks.

Findings

Achieved 100% F1-Score in fault detection on benchmark datasets.

Demonstrated high accuracy in fault and severity classification tasks.

Enhanced model interpretability through symbolic representations and feature attribution.

Abstract

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.