在不同操作条件下使用图形结构数据集表示用于机械故障诊断的权重域调整
Junhyuk Choi1, Dohyeon Kong1, Hyunbo Cho1
1Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
这项研究引入了一种新的加权域适应方法,用于可靠的故障诊断. 它通过利用制造元数据和原始数据关系来改善工业机器诊断来提高准确性.
科学领域:
- 机器学习 机器学习
- 工业工程 工业工程 工业工程
- 数据科学数据科学数据科学
背景情况:
- 数据驱动的故障诊断在现实世界工业环境中扎着不断变化的数据分布.
- 现有的域调整方法往往忽略关键的制造元数据,并统一地处理所有数据域.
研究的目的:
- 开发一种先进的加权域适应方法用于故障诊断,有效地利用元数据.
- 为了提高诊断性能,尽管操作条件和数据分布的变化.
主要方法:
- 建议使用图形结构数据集表示来编码数据集之间的关系,使用元数据和原始数据.
- 源数据集的可转移性得分是使用图形嵌入模型估计的.
- 对于最终的故障诊断模型,使用了基础分类器的加权投票组合.
主要成果:
- 与现有的域适应技术相比,拟议的方法在旋转机械故障检测方面表现优越.
- 关于工具磨损和轴承故障检测的案例研究验证了基于图形的加权域调整方法的有效性.
结论:
- 新的加权域适应方法显著提高了数据驱动故障诊断的稳定性和准确性.
- 将制造业元数据纳入图形结构中可以提高诊断模型适应分布变化的适应性.
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