双分支的时空图形网络用于轴承故障诊断
Yajun Wang1,2, Yang Li3,4, Chenggang Li5
1State Energy Group Shendong Coal Group Co., Ltd., Yulin, 719315, China. yajun.wang.h@chnenergy.com.cn.
Scientific reports
|March 12, 2026
概括
这项研究引入了一种新的双分支时空图形网络 (DBSGN),用于诊断旋转机械的轴承故障. 通过有效分析复杂的振动信号,DBSGN提高了故障检测的准确性和稳定性.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 旋转机械的轴承故障会造成重大损坏和安全风险.
- 传统的振动分析与杂,不规则的信号作斗争,以有效地提取断层特征.
- 需要先进的方法来改善轴承故障诊断.
研究的目的:
- 提出一种使用双分支时空图形网络 (DBSGN) 的新型轴承故障诊断方法.
- 解决传统方法在从复杂的振动信号中提取高维特征方面的局限性.
- 提高轴承故障诊断的准确性和可靠性.
主要方法:
- 使用光谱理论建模的振动信号,用于构建时空图.
- 基于拉普拉斯的光谱分解用于特征向量提取.
- 一个具有动态注意力机制的双分支融合网络,旨在进行模型培训和验证.
主要成果:
- 与传统模型相比,拟议的DBSGN方法显示出更高的性能.
- 三个基准数据集的实验结果证实了DBSGN的增强稳定性和准确性.
- 该方法有效地从噪音和不规则的振动信号中提取特征.
结论:
- DBSGN为轴承故障诊断提供了强大而准确的解决方案.
- 这种方法提高了旋转机械的可用性,可靠性和安全性.
- 时空图形网络显示了复杂机器诊断的巨大潜力.
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