航空轴承的故障特征提取方法基于最大相关性Re'nyi和相位空间重建技术
Zhen Zhang1, Baoguo Liu2, Yanxu Liu2
1School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究引入了一种用于提取复合故障信号特征的新方法,即使信号质量低. 该技术通过提高噪声抑制和特征灵敏度来增强故障检测.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 错误诊断 错误诊断 错误诊断 是一个
背景情况:
- 在低信号噪声比 (SNR) 和复杂噪声下,提取复合断层特征具有挑战性.
- 现有的方法很难有效地抑制噪声,同时保持故障灵敏度.
研究的目的:
- 为复合故障信号提出一种新的特征提取方法.
- 在杂的环境中提高故障诊断的准确性.
主要方法:
- 阶段空间重建与最大相关性相结合的雷尼 entropy deconvolution.
- 使用单值分解 (SVD) 进行噪声抑制和信号分解.
- 使用雷尼 Entropy 作为平衡噪声稳定性和故障灵敏性的性能指数.
主要成果:
- 拟议的方法有效地从低SNR下的复合故障信号中提取特征.
- 与现有技术相比,在降噪和故障灵敏度方面表现出卓越的性能.
- 通过模拟,实验数据和实验室测试的验证证实了该方法的有效性.
结论:
- 开发的特征提取方法显著改善了复合故障信号的分析.
- 这种方法在具有挑战性的工业条件下为故障诊断提供了可靠的解决方案.
- 整合最大相关性雷尼 entropy deconvolution 提供了增强的诊断能力.
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