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基于EMD-SSSC分解的振动信号去势

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提出一种基于经验模态分解和软筛分准则的振动信号去势方法EMD-SSSC(Empirical Mode Decomposition-Soft Sifting Stopping Criterion,EMD-SSSC),根据软筛分准则自适应控制筛分过程,改善本征模态函数的混叠问题,提高经验模态分解的精度与效率,从而有效去除振动信号的趋势项.通过解析函数和两自由度弹簧-质量-阻尼系统验证该方法的有效性和精度,并进一步将其应用于实际岸桥结构健康监测中加速度响应的去势.结果表明:采用EMD-SSSC方法可以准确剔除振动信号中的趋势项,去势精度远高于最小二乘法;所提出方法既可有效用于数值积分中因积分常数的存在而出现的趋势项,也可合理去除工程实际监测信号中的趋势项.
Detrend of Vibration Signals Based on EMD-SSSC Decomposition
A new method for removing the trend of vibration signals based on Empirical Mode Decomposition and Soft Sifting Stopping Criterion(EMD-SSSC)is proposed.EMD-SSSC improves IMF' s aliasing problem through the soft screening stopping criterion and adaptively controls the screening process.By EMD-SSSC,the accuracy and efficiency of EMD are raised,and the trend terms in the vibration signals are effectively removed.The precision,feasibility and effectiveness of the proposed method are verified by an analytical function and a 2-DOF spring-mass-damper system.The method is also utilized to remove the trend of monitoring acceleration signals of a practical quayside container crane.The results show that the proposed EMD-SSSC is capable for removing the trend much more accurately than the least-squares method,and can remove the trend caused by numerical integration,as well as the trend of practical monitoring signals.

vibration and waveEMDsoft sieving stopping criterionsignal detrendsignal processingquayside container crane monitoring

杨穹、秦仙蓉、刘兆航、孙远韬、张氢

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同济大学 机械与能源工程学院,上海 201804

振动与波 经验模态分解 软筛分停止准则 信号去势 信号分析 岸桥监测

2024

噪声与振动控制
中国声学学会

噪声与振动控制

CSTPCD北大核心
影响因子:0.622
ISSN:1006-1355
年,卷(期):2024.44(1)
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