基于KAN卷积和双分支融合的可变运行条件的轴承故障诊断注意事项
Qibing Wang1, Chuanjie Yin1, Kun She2
1College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, 310018, China.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种使用科尔莫戈罗夫-阿诺德卷积网络 (KANConv) 和注意力机制的新型轴承故障诊断方法. 在各种条件下,KANConv-ACGCA-SENet模型在识别轴承故障方面实现了高精度.
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:
- 轴承故障在旋转机械中至关重要,需要准确的诊断方法.
- 传统的卷积神经网络 (CNN) 在捕捉复杂的故障特征方面存在局限性.
- 开发针对可变工作条件的强大的诊断工具是必不可少的.
研究的目的:
- 提出一种先进的轴承故障诊断方法,使用一种新的CNN架构.
- 增强不同操作场景的特征学习和模型稳定性.
- 为了验证拟议的KANConv-ACGCA-SENet模型的有效性.
主要方法:
- 在CNN中实施了一个新的Kolmogorov-Arnold网络 (KAN) 结构,取代了线性内核.
- 引入了一个双分支的自适应上下文感知图形通道注意 (ACGCA) 模块,具有非本地操作.
- 综合挤压和激发网络 (SENet) 用于全球功能改进.
主要成果:
- 在单次负载场景中实现了99.63%的平均准确性.
- 在可变工作条件场景中达到99.05%的平均准确性.
- 证明了高诊断准确度和优越的概括能力.
结论:
- 拟议的KANConv-ACGCA-SENet方法为轴承故障诊断提供了一个强大的替代方案.
- 新的KANConv架构显著提高了诊断性能.
- 该模型即使在具有挑战性的,可变的操作条件下也被证明是有效的.
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