一种创新的跨领域机械故障诊断方法,通过视觉变压器将声波和振动信号融合在一起
Zhenyun Chu1, Shuo Xing1, Baokun Han1
1College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
这项研究引入了一种双通道并行对抗网络 (DPAN),用于稳固的轴承故障诊断. DPAN增强了从声波和振动信号中提取特征,在不断变化的操作条件下提高了诊断准确性.
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:
- 由于操作条件的变化,信号特征的转移会降低轴承故障诊断的准确性.
- 现有的模型在动态环境中难以实现功能稳定性和通用性.
- 需要先进的深度学习架构来实现可靠的条件监控.
研究的目的:
- 提出一种新的双通道并行对抗网络 (DPAN),用于增强轴承故障诊断.
- 提高诊断模型对操作变化的稳定性和概括能力.
- 在实验验证中达到超过98%的高诊断准确度.
主要方法:
- 开发了一个双通道并行对抗网络 (DPAN),利用视觉变换器.
- 通过并行网络通道从声波和振动信号中提取特征.
- 雇佣了对抗性培训和瓦瑟斯坦距离,用于特征融合和域调整.
主要成果:
- 与现有方法相比,DPAN在轴承故障诊断实验中表现出更高的诊断准确性.
- 实验结果证实了该方法的有效性,达到 98% 以上的诊断准确度.
- 敌对训练和瓦瑟斯坦距离显著提高了功能稳定性和网络通用性.
结论:
- 拟议的DPAN有效地解决了轴承故障诊断中信号特征转移的挑战.
- 该方法为动态工业环境中的状态监控提供了强大而准确的解决方案.
- DPAN显示了提高机器诊断可靠性的巨大潜力.
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