一种具有当前信号诊断方法的新方法,结合了变量模态分解和改进的随机森林
Heyu Zhang1, Yuqiao Zheng1, Jieshan Lu1
1School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
The Review of scientific instruments
|February 27, 2024
概括
这项研究引入了一种使用轴承电流信号的先进轴承故障诊断方法. 这种新的方法在识别轴承故障方面实现了高精度 (超过95%),即使噪音很大.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 轴承故障在旋转机械中至关重要,导致昂贵的停机时间.
- 传统的故障诊断方法与杂的轴承电流信号作斗争.
- 准确而强大的故障诊断对于预测性维护至关重要.
研究的目的:
- 开发一种使用当前信号对轴承进行新型故障诊断方法.
- 为了提高轴承故障检测的准确性和可靠性.
- 为了应对当前信号中强大的背景噪声所带来的挑战.
主要方法:
- 轴承电流信号使用变量模态分解 (VMD) 进行了分解.
- 内在模式函数 (IMFs) 是使用kurtosis作为特征向量构建的.
- 一个随机森林 (RF) 分类器被鱼优化算法 (WOA) 优化为故障诊断.
主要成果:
- 拟议的方法实现了对真实损坏的轴承故障类型的分类准确率为95.11%.
- 对于手工损坏的轴承,故障诊断准确率达到93.83%.
- 与传统模型相比,WOA优化的射频模型表现出优越的性能.
结论:
- 开发的故障诊断方法显示了高精度和强大的概括能力.
- 优化VMD和WOA的RF有效地处理噪声信号,可靠地检测轴承故障.
- 这种方法为工业轴承健康监测提供了一个有希望的解决方案.
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