基于自适应软值收缩的增强K-SVD消噪算法,用于风力轮机滚动轴承故障检测
Jimeng Li1, Ze Wang1, Qiang Li1
1College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, PR China.
ISA transactions
|August 11, 2023
概括
本研究介绍了一种改进的K-SVD消噪方法,用于风力轮发电机轴承故障检测. 该技术有效地从噪音数据中提取冲动信号,提高了故障诊断的准确性.
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:
- 风力轮发电机轴承在非静止条件和恶劣环境下工作.
- 轴承故障的冲动特征通常会被显著的噪音所掩盖,使故障检测复杂化.
- 稀疏表示对于非静止信号是有效的,但对字典原子相似性敏感.
研究的目的:
- 开发一种高精度的方法,从杂的轴承数据中提取脉冲信号.
- 改进风力轮发电机轴承故障检测的K-SVD无噪技术.
- 在具有挑战性的操作条件下提高滚动轴承故障诊断的准确性.
主要方法:
- 一种增强的K-SVD无雾化方法,采用自适应软值收缩.
- 适应性稀疏编码收缩软值表示噪声和和干扰删除在字典更新期间.
- 一个软值收缩函数与一个自适应值,以优化辞典的冲动信号恢复.
主要成果:
- 拟议的方法有效地消除噪声和波干扰,揭示了字典原子中的明显冲击特征.
- 获得了一个优化的字典,导致了优异的冲动信号恢复.
- 在两个工程案例中,信封频谱相关性kurtosis显著超过了比较方法.
结论:
- 增强的K-SVD方法在检测风力轮机滚动轴承故障方面表现出优越性.
- 适应性稀疏编码和软值收缩有效地增强了冲动特征提取.
- 该方法提供了一个强大的解决方案,用于在具有挑战性的风力轮机操作环境中准确诊断故障.
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