一种基于Blaschke的多变量模式分解方法用于轮故障诊断
Xianbin Zheng1, Zhengyang Cheng1, Junsheng Cheng1
1College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
一种新的基于Blaschke的多变量模式分解 (MBMD) 方法通过整合多变量振动信号来改善轮系统故障诊断. 这种方法增强了机械故障特征的提取,并提供比现有技术更准确的诊断结果.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 状态监控 状态监控
背景情况:
- 现有的多变量信号分解方法缺乏对轮系统的机械洞察力,阻碍了有效的故障特征提取.
- 轮系统的准确故障诊断对于防止灾难性故障和确保运行可靠性至关重要.
研究的目的:
- 提出一种新的基于Blaschke的多变量模式分解 (MBMD) 方法,用于增强轮系统故障诊断.
- 通过将轮系统的机械特性纳入信号分解来解决当前方法的局限性.
主要方法:
- 模拟多变量振动信号作为多维轮系统响应.
- 利用静态适应里埃分解 (SAFD) 通过布拉什克产品来表示信号,从而实现适应性的多通道信息融合.
- 介绍了Blaschke多光谱和用于模态对齐和频谱分割的联合光谱分割算法.
- 采用以投票为基础的过器银行,通过轮故障机制获取信息,以抑制噪音和功能增强.
主要成果:
- MBMD有效地整合了来自轮系统振动的多变量信息.
- 与现有技术相比,拟议的方法在断层特征提取方面表现出优异的性能.
- 实验验证证了MBMD在轮故障诊断中的有效性.
结论:
- MBMD为轮系统的机械故障诊断提供了一种新且有效的方法.
- 该方法能够整合多变量信息并考虑机械特性,从而更准确地检测故障.
- MBMD为推进旋转机械的状态监测和诊断提供了一个新的视角.
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