Study on Denoising and Blind Separation of Fatigue Crack Propagation Acoustic Emission
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摘要: 采用声发射技术评估疲劳裂纹扩展状态时,评估结论会受到其它类型声发射信号和噪声的干扰。针对上述问题,在分析经验模态分解和独立分量分析特点的基础上,提出集合导数优化经验模态分解与独立分量分析相结合的声发射信号去噪盲分离方法,用于疲劳裂纹扩展声发射信号的处理。分别进行模拟声发射信号和疲劳裂纹扩展试验,采用上述方法对采集声发射信号进行去噪盲分离,结果表明:基于集合导数优化经验模态分解与独立分量分析的声发射信号去噪方法可有效去除噪声信号的干扰,准确分离出疲劳裂纹扩展声发射信号,为进行含裂纹结构的疲劳损伤状态评估和剩余寿命预测奠定基础。
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关键词:
- 声发射信号 /
- 去噪盲分离 /
- 集合导数优化经验模态分解 /
- 独立分量分析 /
- 疲劳裂纹扩展
Abstract: This paper proposed a new method for signal denoising and blind separation for fatigue crack propagation acoustic emission, combined ensemble derivative optimization empirical mode decomposition with independent component analysis, based on the analysis of the characteristics of empirical mode decomposition and independent component analysis. Simulated acoustic emission signal and fatigue crack growth test are respectively carried out, the collected acoustic emission signals are de-noised and blind separated by the proposed method, the results show that the interference signals are removed effectively by the method based on ensemble derivative optimization empirical mode decomposition and independent component analysis, the fatigue crack propagation acoustic emission signal is separated accurately. This study lays a foundation of fatigue damage evaluation and residual life prediction. -
表 1 AE信号主分量协方差特征值
特征值 λ1 λ2 λ3 λ4 λ5 λ6 λ7 λ8 数值 434.13 182.63 61.48 1.65 0.57 0.33 0.12 5.6×10-7 表 2 小波-ICA去噪盲分离波形相关系数
小波-ICA s1(t)- s2(t)- s3(t)- 相关系数 0.809 0.864 0.804 表 3 EDEMD-ICA去噪盲分离波形相关系数
EDEMD-ICA s1(t)- s2(t)- s3(t)- 相关系数 0.889 0.973 0.931 -
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