TY  - EJOU
AU  - Yan, Huyong 
AU  - Zhou, Huidong 
AU  - Zheng, Jian 
AU  - Zhou, Zhaozhe 

TI  - Rolling Bearing Fault Diagnosis Based on 1D Convolutional Neural Network and Kolmogorov–Arnold Network for Industrial Internet
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 3
SN  - 1546-2226

AB  - As smart manufacturing and Industry 4.0 continue to evolve, fault diagnosis of mechanical equipment has become crucial for ensuring production safety and optimizing equipment utilization. To address the challenge of cross-domain adaptation in intelligent diagnostic models under varying operational conditions, this paper introduces the CNN-1D-KAN model, which combines a 1D Convolutional Neural Network (1D-CNN) with a Kolmogorov–Arnold Network (KAN). The novelty of this approach lies in replacing the traditional 1D-CNN’s final fully connected layer with a KANLinear layer, leveraging KAN’s advanced nonlinear processing and function approximation capabilities while maintaining the simplicity of linear transformations. Experimental results on the CWRU dataset demonstrate that, under stable load conditions, the CNN-1D-KAN model achieves high accuracy, averaging 96.67%. Furthermore, the model exhibits strong transfer generalization and robustness across varying load conditions, sustaining an average accuracy of 90.21%. When compared to traditional neural networks (e.g., 1D-CNN and Multi-Layer Perceptron) and other domain adaptation models (e.g., KAN Convolutions and KAN), the CNN-1D-KAN consistently outperforms in both accuracy and F1 scores across diverse load scenarios. Particularly in handling complex cross-domain data, it excels in diagnostic performance. This study provides an effective solution for cross-domain fault diagnosis in Industrial Internet systems, offering a theoretical foundation to enhance the reliability and stability of intelligent manufacturing processes, thus supporting the future advancement of industrial IoT applications.
KW  - Bearing fault diagnosis; Kolmogorov–Arnold network; adaptivity under various working load; transfer generalization

DO  - 10.32604/cmc.2025.062807