模糊透辅助解卷法及其用于轴承故障诊断的应用
Di Pei1, Jianhai Yue1, Jing Jiao2
1School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China.
Entropy (Basel, Switzerland)
|April 26, 2024
概括
一种新的模糊辅助解卷法 (FEAD) 增强了振动信号中的弱轴承故障冲动. 这种方法有效地抑制噪音,提高轴承故障诊断的准确性.
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:
- 振动信号分析对于轴承故障诊断至关重要.
- 振动信号中的弱故障冲动往往被噪声和复杂的传输路径所掩盖.
- 现有的盲式解卷 (BD) 方法难以有效地解决残余噪声问题.
研究的目的:
- 为提高轴承故障诊断提出一种新的模糊辅助解卷 (FEAD) 方法.
- 为了改善解卷过程中的残余噪声的抑制.
- 为了提高在轴承振动信号中的弱故障冲动的检测.
主要方法:
- 开发了一个模糊的辅助解卷 (FEAD) 方法.
- 构建了一个加权的FuzzyEn-kurtosis目标函数来增强冲动和抑制噪音.
- 使用粒子集群优化 (PSO) 算法来解决最佳解卷过器的问题.
- 应用信封频谱分析用于最终故障诊断.
主要成果:
- FEAD有效地增强了故障冲动,同时显著抑制了噪音干扰.
- 在模拟信号,Case Western Reserve University (CWRU) 轴承数据,铁路轮组和测试台上进行验证.
- 通过定量分析,与最先进的盲式解卷方法相比,表现出优异的性能.
结论:
- 拟议的FEAD方法提供了一个强大的解决方案,用于增强轴承振动分析中的弱故障冲动.
- FEAD在噪声抑制方面具有显著的优势,导致更准确的轴承故障诊断.
- 该方法在各种操作条件和数据集中表现出色.
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