受测试环境影响,隧道爆破监测信号中普遍包含噪声和趋势项干扰。针对爆破信号干扰项消除难题,选取典型地铁隧道工程监测到的畸变爆破信号为分析对象,采用稀疏化基线估计与去噪(baseline estimation and denoising with sparsity,BEADS)...受测试环境影响,隧道爆破监测信号中普遍包含噪声和趋势项干扰。针对爆破信号干扰项消除难题,选取典型地铁隧道工程监测到的畸变爆破信号为分析对象,采用稀疏化基线估计与去噪(baseline estimation and denoising with sparsity,BEADS)算法实现了噪声和趋势项成分的提取,得到反映真实爆破信息的校正信号。利用多重分形去趋势波动分析(multi-fractal detrended fluctuation analyses,MF-DFA)捕捉到三个分量信号的混沌分形特征,并根据小波相关性凝聚谱对三个分量信号与原始信号的时频域相关性进行了精确表征。结果表明:隧道爆破信号高频噪声、低频趋势项和校正信号三者的混沌分形特征具有显著差异。校正信号吸引子轨迹形态为反复周期性有序波动且具有持续性和反持续性分形谱特征,其递归图具有周期模式;低频趋势项吸引子形态表现为近似直线且具有持续性分形谱特征,其递归图具有对角线分布突变模式;高频噪声吸引子形态为杂乱无章的随机波动且具有反持续性分形谱特征,其递归图具有漂移模式。在置信度为95%的小波影响锥范围内,校正信号、趋势项和噪声分量与原始信号分别具有持续正相关、局部负相关和无相关性特征。三类信号的有效分离和混沌分形特征提取为爆破信号成分的准确辨识和归类提供了客观表征和量化指标。展开更多
The accurate estimation of the rolling element bearing instantaneous rotational frequency(IRF) is the key capability of the order tracking method based on time-frequency analysis. The rolling element bearing IRF can b...The accurate estimation of the rolling element bearing instantaneous rotational frequency(IRF) is the key capability of the order tracking method based on time-frequency analysis. The rolling element bearing IRF can be accurately estimated according to the instantaneous fault characteristic frequency(IFCF). However, in an environment with a low signal-to-noise ratio(SNR), e.g., an incipient fault or function at a low speed, the signal contains strong background noise that seriously affects the effectiveness of the aforementioned method. An algorithm of signal preprocessing based on empirical mode decomposition(EMD) and wavelet shrinkage was proposed in this work. Compared with EMD denoising by the cross-correlation coefficient and kurtosis(CCK) criterion, the method of EMD soft-thresholding(ST) denoising can ensure the integrity of the signal, improve the SNR, and highlight fault features. The effectiveness of the algorithm for rolling element bearing IRF estimation by EMD ST denoising and the IFCF was validated by both simulated and experimental bearing vibration signals at a low SNR.展开更多
Poisson-Gaussian noise is the basis of image formation for a great number of imaging systems used in variety of applications, including medical and astronomical imaging. In wavelet domain, the application of Bayesian ...Poisson-Gaussian noise is the basis of image formation for a great number of imaging systems used in variety of applications, including medical and astronomical imaging. In wavelet domain, the application of Bayesian estimation method with generalized Anscombe transform in Poisson-Gaussian noise reduction algorithm has shown remark- able success over the last decade. The generalized Anscombe transform is exerted to convert the Poisson-Gaussian noise into an additive white Gaussian noise （AWGN）. So, the resulting data can be denoised with any algorithm designed for the removal of AWGN. Here, we present simple form of minimum mean square error （MMSE） estimator for logistic distribution in Poisson-Gaussian noise. The experimental results show that the proposed method yields good denoising results.展开更多
文摘受测试环境影响,隧道爆破监测信号中普遍包含噪声和趋势项干扰。针对爆破信号干扰项消除难题,选取典型地铁隧道工程监测到的畸变爆破信号为分析对象,采用稀疏化基线估计与去噪(baseline estimation and denoising with sparsity,BEADS)算法实现了噪声和趋势项成分的提取,得到反映真实爆破信息的校正信号。利用多重分形去趋势波动分析(multi-fractal detrended fluctuation analyses,MF-DFA)捕捉到三个分量信号的混沌分形特征,并根据小波相关性凝聚谱对三个分量信号与原始信号的时频域相关性进行了精确表征。结果表明:隧道爆破信号高频噪声、低频趋势项和校正信号三者的混沌分形特征具有显著差异。校正信号吸引子轨迹形态为反复周期性有序波动且具有持续性和反持续性分形谱特征,其递归图具有周期模式;低频趋势项吸引子形态表现为近似直线且具有持续性分形谱特征,其递归图具有对角线分布突变模式;高频噪声吸引子形态为杂乱无章的随机波动且具有反持续性分形谱特征,其递归图具有漂移模式。在置信度为95%的小波影响锥范围内,校正信号、趋势项和噪声分量与原始信号分别具有持续正相关、局部负相关和无相关性特征。三类信号的有效分离和混沌分形特征提取为爆破信号成分的准确辨识和归类提供了客观表征和量化指标。
基金Project(51275030)supported by the National Natural Science Foundation of ChinaProject(2016JBM051)supported by the Fundamental Research Funds for the Central Universities,China
文摘The accurate estimation of the rolling element bearing instantaneous rotational frequency(IRF) is the key capability of the order tracking method based on time-frequency analysis. The rolling element bearing IRF can be accurately estimated according to the instantaneous fault characteristic frequency(IFCF). However, in an environment with a low signal-to-noise ratio(SNR), e.g., an incipient fault or function at a low speed, the signal contains strong background noise that seriously affects the effectiveness of the aforementioned method. An algorithm of signal preprocessing based on empirical mode decomposition(EMD) and wavelet shrinkage was proposed in this work. Compared with EMD denoising by the cross-correlation coefficient and kurtosis(CCK) criterion, the method of EMD soft-thresholding(ST) denoising can ensure the integrity of the signal, improve the SNR, and highlight fault features. The effectiveness of the algorithm for rolling element bearing IRF estimation by EMD ST denoising and the IFCF was validated by both simulated and experimental bearing vibration signals at a low SNR.
文摘Poisson-Gaussian noise is the basis of image formation for a great number of imaging systems used in variety of applications, including medical and astronomical imaging. In wavelet domain, the application of Bayesian estimation method with generalized Anscombe transform in Poisson-Gaussian noise reduction algorithm has shown remark- able success over the last decade. The generalized Anscombe transform is exerted to convert the Poisson-Gaussian noise into an additive white Gaussian noise （AWGN）. So, the resulting data can be denoised with any algorithm designed for the removal of AWGN. Here, we present simple form of minimum mean square error （MMSE） estimator for logistic distribution in Poisson-Gaussian noise. The experimental results show that the proposed method yields good denoising results.
