应用多维同步特征模式分解用于机械故障诊断
Huifang Shi1, Yonghao Miao1, Xun Wang1
1School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.
ISA transactions
|March 6, 2025
概括
本研究引入了一种新的多维同步特征模式分解 (MSFMD) 方法,用于复杂的工业系统中 robust 故障诊断. MSFMD有效地增强了弱故障信号,并处理多故障条件,即使在杂的环境中.
科学领域:
- 工程 工程师 工程师 工程师
- 信号处理 信号处理
- 机器状态监测 机器状态监测
背景情况:
- 复杂的工业系统在故障诊断方面面临挑战,原因是高噪音,干扰和合的多故障特征,特别是在多通道信号处理中.
- 现有的方法,如特征模式分解 (FMD) 和多变量变量模式分解 (MVMD),在多故障场景中与噪音易感性,冗余性和低效性作斗争.
研究的目的:
- 提出和验证一种新的多维同步特征模式分解 (MSFMD) 方法,用于工业系统中增强故障诊断.
- 解决传统方法在处理噪音,多通道信号和复杂的多故障条件方面的局限性.
主要方法:
- MSFMD采用了一个系统的框架,包括使用订单统计过器进行定制的光谱细分.
- 多通道信号与光谱对齐约束的同步分解捕获了通道间的相关性.
- 基于时间频率相关系数和包膜光谱曲解的自适应模式选增强了特征稳定性.
主要成果:
- MSFMD有效地放大了弱故障特征,并处理复杂的多故障条件.
- 该方法通过减少噪音和冗余模式来证明提取的特征的改进稳定性和可解释性.
- 与口病和病相比,MSFMD表现优越,特别是在高噪音和多断层环境中.
结论:
- 该MSFMD方法为复杂的工业系统提供准确可靠的故障诊断.
- 在具有挑战性的高噪音环境中,MSFMD提供了更高的计算效率和诊断能力.
- 经过验证的MSFMD方法代表了工业故障诊断的多通道信号处理的重大进步.
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