评估手工特征提取用于旋转机械故障诊断:一项调查
René-Vinicio Sánchez1, Jean Carlo Macancela1, Luis-Renato Ortega1
1GIDTEC, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
这项研究引入了用于状态监控的手工制造功能,证明了在旋转机器故障诊断方面的有效性. 这些特征为复杂的方法提供了低成本,可解释的替代方案.
科学领域:
- 工程 工程师 工程师 工程师
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 旋转机械的状态监控对于防止故障至关重要.
- 传统的信号处理方法往往需要专业知识的特征工程.
- 在故障分类中,评估手工制作特征的有效性至关重要.
研究的目的:
- 为状态监测信号提供一系列手工制作的功能.
- 开发和应用基于机器学习的方法来评估故障分类中的特征性能.
- 确定用于旋转机器故障诊断的最有效的手工制作功能.
主要方法:
- 从状态监控信号中提取了123个时间域和46个频域特征.
- 采用七种排名方法,从七个旋转机器数据集中选择顶级特征.
- 使用三个不同的分类器类型评估选定的特征,并结合外部基准.
主要成果:
- 证明了手工制作特征的有效性,以高精度进行故障分类.
- 确定了在不同数据集和排名方法中持续优于其他数据集的特定特征.
- 通过图形分析展示了某些特征的流行程度和重要性.
结论:
- 手工特征提取是旋转机械故障识别和诊断的可行和有效的技术.
- 拟议的方法提供了一种系统的方法来选择功能和绩效评估.
- 手工制作的功能提供了较低的计算成本和高的可解释性,使它们适合于实际的条件监测应用.
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