条件增强变量自编码器-异质图注意力神经网络:基于异质信息的电动轮的新型故障诊断方法
Ximing Cao1, Ruifeng Yang1, Chenxia Guo1
1School of Instrument and Electronics, North University of China, Taiyuan 030051, China.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
这项研究介绍了CEVAE-HGANN,这是一种使用多传感器数据进行机器故障诊断的新型模型. 它有效地处理异质数据,以改善电动诊断.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 机器故障诊断依赖于多传感器数据用于图形构造.
- 现有的图形数据驱动的方法与异质的多传感器数据作斗争.
研究的目的:
- 提出CEVAE-HGANN,这是一个创新的模型,用于在故障诊断中高效的异质多传感器数据处理.
- 通过有效地表示子系统之间的复杂相互关系,增强电动轮故障诊断.
主要方法:
- 条件增强的变量自编码器 (CEVAE) 用于特征交互和维度缩小.
- 定义元路径和使用欧几里德距离/皮尔森系数来构建相邻矩阵.
- 异质图注意力神经网络 (HGANN) 用于分类,专注于子系统互连.
主要成果:
- CEVAE-HGANN模型证明了对异质多传感器数据的高效处理.
- 该模型有效地捕捉了子系统之间的复杂相互关系.
- 实验结果验证了CEVAE-HGANN模型在电动轮故障诊断方面的优越性.
结论:
- CEVAE-HGANN提供了一种强大的解决方案,用于用异质的多传感器数据进行机器故障诊断.
- 该模型的方法超越了节点级别的识别,以捕捉关键的子系统相互作用.
- 这种方法显著提高了电动轮故障诊断的准确性和效率.
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