滚动轴承故障诊断基于细粒度多尺度的科尔摩戈罗夫和WOA-MSVM
Heliyon
|March 22, 2024
概括
这项研究引入了一种新的故障诊断技术,用于使用细粒度多尺度科尔摩戈罗夫和鱼优化多类支向量机器旋转机械. 该方法提高了诊断效率和轴承故障检测的速度.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 旋转机器,特别是轴承的有效故障诊断对于操作可靠性和安全性至关重要.
- 传统的方法经常与复杂的振动信号和诊断模型的参数优化作斗争.
- 在实时故障检测应用中,现有的技术可能缺乏效率和速度.
研究的目的:
- 为旋转机械提出一个先进的故障诊断技术.
- 提高轴承故障诊断的准确性和效率,使用一种新的特征提取和分类方法.
- 在多类支向量机器模型中解决参数灵敏性的挑战.
主要方法:
- 振动信号是使用细粒度多尺度分解分解的.
- 科尔摩戈罗夫被计算为子信号,以创建多维特征向量,特征信号复杂性.
- 鱼优化算法 (WOA) 优化了多类支持向量机 (MSVM) 的惩罚因子和内核函数参数,创建了FGMKE-WOA-MSVM模型.
主要成果:
- 拟议的FGMKE-WOA-MSVM技术在故障诊断方面表现出优异的性能,与K-近邻 (KNN) 和随机森林 (RF) 相比.
- 该方法在江南大学轴承数据集上实现了快速计算速度和高诊断效率.
- 来自多尺度科尔摩戈罗夫的特征向量有效地捕获了信号复杂性,以改进分类.
结论:
- FGMKE-WOA-MSVM技术为旋转机械故障诊断提供了强大而高效的解决方案.
- 先进的信号分解,特征提取和优化的SVM的结合提供了高诊断准确度.
- 这种方法在工业设备的状态监测和预测性维护方面取得了重大进展.
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