一种用于离心故障检测和识别的技术,基于一种新的故障特定的曼-惠特尼测试
Zahoor Ahmad1, Jae-Young Kim2, Jong-Myon Kim1,2
1Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
这项研究引入了一种新的方法来检测和识别离心故障,使用一种新的故障特定的曼·惠特尼测试和K-最近邻居分类. 这种方法提高了故障检测的灵敏度和准确性,即使在不同的操作条件下.
科学领域:
- 机械工程 机械工程
- 振动分析 振动分析
- 状态监控 状态监控
背景情况:
- 离心 (CP) 的传统故障指示器缺乏用于早期故障检测的灵敏度.
- 监督的缺陷识别模型需要预先了解故障,从而限制了它们的适用性.
- 在CP中初始故障检测和识别仍然具有挑战性,特别是在可变的操作条件下.
研究的目的:
- 为CP开发一种不依赖于先前知识的新型故障指标.
- 提高CP中故障检测和识别的灵敏度和准确性.
- 在可变的操作条件下改善故障分类性能.
主要方法:
- 一个新的离心故障指标 (CPFI) 是使用一种新的故障特定的曼-惠特尼测试开发的.
- 振动信号 (VS) 通过波束包转换 (WPT) 进行分层分解,以选择故障特定的频段.
- 对于故障类型的分类,采用了K-最近邻居 (KNN) 分类算法,并结合了效应比 (ER).
主要成果:
- 新的故障特异性曼·惠特尼测试产生了对CP的健康状况和操作条件敏感的CPFI.
- 拟议的方法在故障分类准确度方面显示出有前途的改进,特别是在可变的操作条件下.
- 结合WPT,曼-惠特尼测试和KNN分类,有效地解决了传统方法的局限性.
结论:
- 开发的技术为离心的故障检测和识别提供了强大而灵敏的方法.
- 新的CPFI和KNN分类提供了增强的诊断能力,特别是在动态的操作环境中.
- 该方法推进了旋转机械的状态监测技术,降低了维护成本和停机时间.
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