KAN-HyperMP:在杂环境中用于滚动轴承的增强故障诊断模型
Jun Wang1, Zhilin Dong2, Shuang Zhang3
1Department of Ocean Engineering, Yantai Institute of Science and Technology, Yantai 265600, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
一个新的基于Kolmogorov-Arnold网络的超图信息传递 (KAN-HyperMP) 模型有效地诊断滚动轴承的故障. 这种方法在噪音条件下表现出色,在基准数据集上实现了高精度.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 滚动轴承是机械中的关键部件.
- 来自轴承的非静态信号容易产生噪声,使故障检测复杂化.
- 现有的方法难以应对高噪音环境.
研究的目的:
- 为滚动轴承开发一种新的故障诊断方法.
- 为了提高在噪音条件下故障检测的准确性.
- 为了引入基于Kolmogorov-Arnold网络的超图信息传递 (KAN-HyperMP) 模型.
主要方法:
- 利用超图理论进行邻近特征聚合,整合远程信息以减轻噪音.
- 采用特征融合块,将高阶邻近特征与目标节点特征结合起来.
- 利用Kolmogorov-Arnold网络 (KAN) 和B-spline功能从噪声信号中进行强大的特征提取.
主要成果:
- KAN-HyperMP模型在SEU数据集上实现了99.70%的高准确率,在JNU数据集上达到99.10%.
- 在无噪声和高噪声条件下,在故障诊断中表现出卓越的性能.
- 从复杂,杂的轴承信号中成功提取了关键诊断特征.
结论:
- 拟议的KAN-HyperMP模型对于滚动轴承故障诊断非常有效.
- 该型号的架构对噪音具有坚固耐用性,在充满挑战的环境中提供可靠的性能.
- 这种方法在旋转机械的状态监测和预测性维护方面取得了重大进展.
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