精制复合材料多尺度斜率和其在滚动轴承故障诊断中的应用
Junfeng Wang1, Jinde Zheng1, Haiyang Pan1
1School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China.
ISA transactions
|August 2, 2024
概括
一种新的方法,精炼复合多尺度斜率 (RCMSlE),通过分析复杂的振动信号来增强滚动轴承故障诊断. 这种方法准确地识别出故障,与现有方法相比,提高了效率.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 非线性动力学是一种非线性动力学.
背景情况:
- 滚动轴承在旋转机械中至关重要,但它们的振动信号在故障时变得非线性和非静止.
- 现有的方法,如多尺度转换 (MPE) 在捕获振幅信息和粗粒度的限制.
- 需要改进的分析方法来准确诊断轴承故障.
研究的目的:
- 为滚动轴承故障诊断提出一种新的非线性动态分析方法,即精制复合多级斜坡透 (RCMSlE).
- 通过结合振幅和模式信息来增强轴承振动信号复杂性和规律性的分析.
- 开发一个智能故障诊断系统,将RCMSlE与GWO-SVM分类器结合起来.
主要方法:
- 通过引入精细的复合概念和捕捉振幅和模式的新符号表示来开发RCMSlE.
- 集成的RCMSlE与灰狼优化器支持向量机 (GWO-SVM) 多分类器用于故障识别.
- 使用各种故障条件的滚动轴承的实验数据验证了拟议的方法.
主要成果:
- 该RCMSlE方法有效地分析轴承振动信号的复杂性和规律性,克服MPE的局限性.
- 结合RCMSlE和GWO-SVM的方法在识别不同类型和程度的滚动轴承故障方面表现出高准确性.
- 提出的智能诊断方法表现出较短的计算时间和比较技术的优异性能.
结论:
- RCMSlE是分析非线性和非静止轴承信号的强大工具,提供了改进的故障诊断功能.
- 将RCMSlE与GWO-SVM集成,为滚动轴承提供了有效和高效的智能故障诊断解决方案.
- 这项研究推进了旋转机械的状态监测和故障诊断领域.
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