主电机屏蔽套的故障诊断方法基于注意力机制和多源数据融合
Nengqing Liu1, Xuewei Xiang1, Hui Li1
1State Key Laboratory of Power Transmission and Transformation Equipment Technology, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
这项研究引入了一种新的故障诊断方法,用于主电机屏蔽套,利用注意力机制和多源数据融合. 与现有方法相比,这种方法显著提高了诊断准确度,确保了更好的设备可靠性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 状态监控 状态监控
背景情况:
- 主电机中的屏蔽袖面临复杂的操作环境,导致像凸起和裂这样的故障.
- 空间有限阻碍了传感器安装,以有效监测状态.
- 现有的故障诊断方法在多个尺度的特征提取和多个来源的数据融合方面扎.
研究的目的:
- 提出一种先进的故障诊断方法,用于屏蔽套件故障.
- 通过整合注意力机制和多源数据融合来解决当前方法的局限性.
- 即使单个数据源具有弱故障特征,也可以进行准确的诊断.
主要方法:
- 开发了一种基于注意力的多尺度卷积神经网络 (AM-MSCNN),用于丰富的特征提取.
- 提出了基于注意力的卷积神经网络,用于多尺度和多源数据融合 (AM-MSMDF-CNN),以集成扭矩,速度,电压和电流数据.
- 利用BP算法和交叉损失进行最终的故障分类.
主要成果:
- 在故障诊断方面,AM-MSMDF-CNN方法表现出卓越的性能.
- 与1D-CNN,包装,随机森林和SVM相比,在模拟数据上获得了5-10%的更高准确性,在实验数据上获得了10-15%的更高准确性.
- 通过有限元模拟和小规模原型实验验证实有效性.
结论:
- 拟议的AM-MSMDF-CNN方法为屏蔽套的故障诊断提供了强大而准确的解决方案.
- 整合注意力机制和多源数据融合对于处理复杂故障特征至关重要.
- 这种方法在具有挑战性的操作条件下提高了设备的可靠性.
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