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.

翻译：滚动轴承是旋转机械的关键部件，其性能直接影响工业系统的效率与可靠性。同时，轴承故障是引发机械故障的主要原因，常导致昂贵的停机时间、生产力下降，极端情况下甚至造成灾难性损坏。本研究提出一种利用Kolmogorov-Arnold网络的方法，通过自动特征选择、超参数调优和可解释故障分析，在统一框架内应对这些挑战。通过训练浅层网络架构并最小化所选特征数量，该框架构建出轻量化模型，借助特征归因和激活函数的符号化表示提供可解释的结果。在两个广泛认可的轴承故障诊断数据集上进行验证，该框架在故障检测任务中取得了完美的F1分数，在故障类型与严重程度分类任务中也表现出高性能，多数情况下达到100%的F1分数。值得注意的是，该框架展现出良好的适应性，能够处理同一数据集中不平衡、不对中等多种故障类型。符号化表示增强了模型的可解释性，而特征归因则揭示了各研究任务中最优特征类型或信号。这些结果凸显了该框架在实时机械监测等实际应用，以及需要高效可解释模型的科学研究中的潜力。