Wang Jun, Dong Zhilin, Zhang Shuang
Department of Ocean Engineering, Yantai Institute of Science and Technology, Yantai 265600, China.
School of Engineering, Zhejiang Normal University, Jinhua 321004, China.
Sensors (Basel). 2024 Oct 5;24(19):6448. doi: 10.3390/s24196448.
Rolling bearings often produce non-stationary signals that are easily obscured by noise, particularly in high-noise environments, making fault detection a challenging task. To address this challenge, a novel fault diagnosis approach based on the Kolmogorov-Arnold Network-based Hypergraph Message Passing (KAN-HyperMP) model is proposed. The KAN-HyperMP model is composed of three key components: a neighbor feature aggregation block, a feature fusion block, and a KANLinear block. Firstly, the neighbor feature aggregation block leverages hypergraph theory to integrate information from more distant neighbors, aiding in the reduction of noise impact, even when nearby neighbors are severely affected. Subsequently, the feature fusion block combines the features of these higher-order neighbors with the target node's own features, enabling the model to capture the complete structure of the hypergraph. Finally, the smoothness properties of B-spline functions within the Kolmogorov-Arnold Network (KAN) are employed to extract critical diagnostic features from noisy signals. The proposed model is trained and evaluated on the Southeast University (SEU) and Jiangnan University (JNU) Datasets, achieving accuracy rates of 99.70% and 99.10%, respectively, demonstrating its effectiveness in fault diagnosis under both noise-free and noisy conditions.
滚动轴承经常产生非平稳信号，这些信号很容易被噪声掩盖，尤其是在高噪声环境中，这使得故障检测成为一项具有挑战性的任务。为应对这一挑战，提出了一种基于基于柯尔莫哥洛夫 - 阿诺德网络的超图消息传递（KAN - HyperMP）模型的新型故障诊断方法。KAN - HyperMP模型由三个关键组件组成：邻域特征聚合块、特征融合块和KANLinear块。首先，邻域特征聚合块利用超图理论整合来自更远邻域的信息，即使附近邻域受到严重影响，也有助于降低噪声影响。随后，特征融合块将这些高阶邻域的特征与目标节点自身的特征相结合，使模型能够捕捉超图的完整结构。最后，利用柯尔莫哥洛夫 - 阿诺德网络（KAN）内B样条函数的平滑特性从噪声信号中提取关键诊断特征。所提出的模型在东南大学（SEU）和江南大学（JNU）数据集上进行训练和评估，分别达到了99.70%和99.10%的准确率，证明了其在无噪声和有噪声条件下故障诊断中的有效性。
