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Open Access

ARTICLE

Rolling Bearing Fault Diagnosis Based on 1D Convolutional Neural Network and Kolmogorov–Arnold Network for Industrial Internet

Huyong Yan1, Huidong Zhou2,*, Jian Zheng1, Zhaozhe Zhou1
1 School of Computer Science and Information Engineering, Chongqing Technology and Business University, Chongqing, 400067, China
2 State Grid Chongqing Shibei Electric Power Supply Branch, Chongqing, 400015, China
* Corresponding Author: Huidong Zhou. Email: email
(This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.062807

Received 28 December 2024; Accepted 24 February 2025; Published online 20 March 2025

Abstract

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.

Keywords

Bearing fault diagnosis; Kolmogorov–Arnold network; adaptivity under various working load; transfer generalization

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