TKAN: A Novel Method for Bearing Fault Diagnosis in Noisy Environments

Rolling bearings, as one of the most vital components in rotating machinery, are frequently exposed to severe noise interference during operation, posing a significant challenge for accurate and rapid fault identification. To address this issue, this study proposes a novel fault diagnosis framework termed TKAN (Transformer-Kolmogorov Arnold Networks), which integrates the global feature extraction capability of the Transformer with the nonlinear noise suppression advantage of the KAN linear layer. In the proposed TKAN model, raw vibration signals are first segmented into structured samples to fully preserve temporal dynamics. A four-layer Transformer module is then employed to extract high-dimensional representations from the input data, leveraging multi-head self-attention to enhance discriminative feature learning across different subspaces. To improve robustness under noisy conditions, a KAN linear layer with B-spline activation is incorporated in place of traditional linear mappings, effectively smoothing the feature space and attenuating noise-induced fluctuations. Extensive experiments are conducted on two widely used benchmark datasets-CWRU and XJTU-to evaluate the performance of TKAN in both clean and noisy environments. Comparative results against five representative deep learning models (MLP, CNN, KAN, LSTM-KAN, and CNN-KAN) demonstrate that TKAN achieves superior performance across multiple evaluation metrics (Accuracy, Precision, Recall and F1-Score). Furthermore, under various levels of Gaussian, Uniform, and Impulse noise, TKAN consistently maintains high classification accuracy, underscoring its strong noise resilience and diagnostic robustness. This study provides a novel approach for fault diagnosis of bearings in noisy environments, offering significant practical and research value.