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通过口病进行轴承故障诊断,参数通过改进的顶峰猪优化器进行优化

Ping Pan¹, Hao Liu¹, Bing Lei²

¹School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.

Sensors (Basel, Switzerland)

|December 11, 2025

概括

这项研究引入了功能模式分解 (FMD) 的改进的Crested Porcupine Optimizer (ICPO),以准确诊断轴承故障. ICPO-FMD方法在噪音条件下增强了故障特征提取,提高了诊断准确性和效率.

科学领域:

机械工程机械工程
信号处理信号处理
优化算法优化算法

背景情况:

功能模式分解 (FMD) 有效地在噪音中提取轴承故障特征.
对于口疾病,手动参数选择 (过器长度L,分解模式K) 是具有挑战性的,并且可能导致错误.
对于口病参数的现有优化算法面临着过早的融合和漫长的计算时间.

研究的目的:

为FMD参数选择开发一个改进的优化算法.
提出一种新的轴承故障诊断方法 (ICPO-FMD),以提高准确性和实时应用.
克服现有的优化方法在FMD参数调整中的局限性.

主要方法:

提议使用一个改进的Crested Porcupine Optimizer (ICPO) 来平衡全球和本地搜索.
ICPO算法优化了FMD参数 (K和L) 进行轴承信号分解.
应用了对故障敏感的IMF选原则,随后进行信号重建和信封解调.

主要成果:

ICPO-FMD方法准确地从轴承信号中提取出故障特征的频率和波.
在模拟,实验室和工程数据上的实验验证证证了该方法的有效性.
拟议的ICPO动态平衡全球和本地利用,以实现高效的参数优化.

结论:

ICPO-FMD方法为轴承故障诊断提供了强大而准确的解决方案.
这种方法减轻了手动FMD参数调整的需要,提高了诊断可靠性.
该方法在提取故障特征方面表现出卓越的性能,即使在强大的噪声干扰下.

关键词:

错误诊断错误诊断错误诊断是一个问题.功能提取特性提取功能模式分解分解改进的尖顶猪优化器滚动轴承的滚动轴承 .

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