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目前,与人体无接触的多通道高温超导量子干涉器能够记录人体心脏表面的磁场强度,在发达国家,相关技术已经被用于临床诊断心肌缺血等疾病。本文利用心脏麟场单磁偶极子模型研究了心脏磁场强度捡测信号的频域空间滤波法。由于心脏磁场呈空间分布,对心脏表面多通道检测信号整体滤波可以参考相关信息。我们用心脏磁场模型的数据分别加5%,10%和20%的高斯白噪声来模拟爱环境噪声影响的检测数据。基于二维频域变换的思想,对心磁信号按行和列进行了空间滤波,并通过数字仿真,计算和比较了滤波前后心磁信号的信噪比(SNR)与拟和优度(GOF),从而验证了这种信号预处理方法的有效性。 相似文献
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与心电圈相比较,心磁检测具有无需电极,对某些局部心肌电流高度敏感,可以用于早期心脏疾病的诊断。但心脏磁场信号在检测过程中会被噪声所污染,使得信号本身的可辨识性降低,因此需要对该信号进行降噪处理。在单通道信号采集系统无噪声参考输入端的情况下采用自适应滤波方法,需要对待处理信号进行线性预测,本文提出的改进LMS(Least Mean Square)算法的自适应预测滤波器,无需噪声参考信号即可对心磁信号进.彳亍滤波,通过三种不同噪声的滤波仿真结果可见,采用自适应预测滤波器处理后明显提高了信噪比,具有一定的学术意义和实用价值。 相似文献
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本文针对去除心磁信号的基线漂移的问题,采用形态学滤波方法,构造了两种结构元素,通过开闭和闭开的平均组合实现了两种有效的数学形态滤波器.数值仿真和信号分析的结果表明:该方法能够很好地保留心磁信号中的T波成分,并能去除心磁信号的基线漂移. 相似文献
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为消除磁瓦图像中的脉冲噪声,提出一种改进的自适应中值滤波算法:该算法通过对小窗口内非噪声点的检测来决定是增大滤波窗口还是选择输出,尽可能地减小了滤波窗口,使得图像细节得到更好地保护;同时该算法对位于窗口中心的疑似噪声点进行二次判别,避免了将信号点误判为噪声点;然后将噪声点按改进的中值滤波输出,而信号点灰度保持不变。仿真实验结果表明,该算法能够有效地滤除磁瓦图像中不同水平的脉冲噪声,并较好地保留原始图像细节信息,较标准中值滤波及其它改进中值滤波算法有更优的滤波性能。 相似文献
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基于数学形态滤波的齿轮故障特征提取方法 总被引:24,自引:2,他引:22
针对齿轮故障特征的提取问题,提出一种根据信号形态特征对齿轮故障信号进行形态滤波的新方法.形态滤波是一种新的非线性滤波方式,可以有效地提取出信号的边缘轮廓以及信号的形状特征.对Lorenz信号进行不同结构元素的数学形态滤波处理,证实形态滤波对抑制信号噪声、保留信号非线性特征方面的作用.采用长度为齿轮冲击周期长度的0.6~0.8倍的扁平结构元素,对齿轮断齿故障振动信号进行形态闭运算处理,并对滤波后的信号进行频谱分析.结果表明,利用形态滤波可以从齿轮断齿信号中成功提取隐含在噪声中的冲击故障特征. 相似文献
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由于常用的形态滤波器采用相同尺寸结构元素而导致的输出存在统计偏移现象以及滤波效果不理想,针对电力系统采样信号,设计采用不同尺寸结构元素级联而成的开-闭和闭-开组合广义形态滤波器实现电力信号的降噪。选取与水平方向夹角为零度的直线型结构元素,比较了广义形态滤波器与常用形态滤波器在脉冲噪声与随机噪声干扰下的降噪效果。仿真表明,广义数学形态滤波器能够更有效地消除噪声干扰,获得更高的信噪比。广义形态滤波降噪算法只涉及加减和极大、极小运算,运算简单且执行高效,具有较好的实用价值。 相似文献
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基于经验模态分解的混沌噪声背景下弱信号检测与信号提取 总被引:8,自引:2,他引:6
基于经验模态分解方法,研究了在强混沌噪声背景下进行弱信号的检测与信号提取。对仿真信号的研究表明:用该方法可以直接提取出微弱的偶然性和周期性冲击时域信号,对弱谐波信号可能不能直接提取,但可以直接提取出其频率特征,这些弱冲击信号和弱谐波信号完全淹没在强的混沌噪声背景信号中,无论从时域上还是频域上基本上都看不出来。对齿轮箱的实际信号的研究也表明:尽管某些故障信号有时极其微弱,EMD方法也能有效地实现这些非线性非平稳信号的分离和提取,从而为机械设备故障诊断提供直观的有效的参考。 相似文献
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V. N. Uvarov G. I. Druzhin D. V. Sannikov 《Instruments and Experimental Techniques》2010,53(6):895-901
A method of recording and detecting closely located natural electromagnetic radiation sources, including sources of lithospheric
origin in seismically active regions is described. The field experiment is performed in a region with low man-caused noise
and high microseismic activity (Kamchatka, Karymshina). A sequence of data demonstrating a large variety of recorded signals
from closely located sources is analyzed. 相似文献
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Niels Henrik Pontoppidan Sigurdur Sigurdsson Jan Larsen 《Mechanical Systems and Signal Processing》2005,19(6):1337-1347
We discuss condition monitoring based on mean field independent components analysis of acoustic emission energy signals. Within this framework, it is possible to formulate a generative model that explains the sources, their mixing and the noise statistics of the observed signals. Using a novelty detection approach based on normal-condition examples only, we detect faulty examples with high precision. The detection is done by evaluating the likelihood that the model, trained with normal examples, generated the signals, compared to a threshold obtained with normal examples. Acoustic emission energy signals from a large diesel engine are used to demonstrate this approach. The experiment show that mean field independent components analysis detects the induced fault with higher accuracy than principal components analysis, while at the same time selecting a more compact model. 相似文献
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基于Duffing振子的噪声背景下微弱周期信号检测 总被引:2,自引:0,他引:2
为有效地实现噪声背景下弱信号的提取,阐述了间歇混沌模型Duffing振子的混沌特性。利用Duffing振子对微弱信号具有敏感性、对噪声与频率差较大的周期干扰信号具有免疫力的特性,研究了基于Duffing振子在噪声条件下检测微弱周期信号、复合频率信号和未知频率信号的方法,用数值仿真验证了该方法的可行性。研究结果表明,基于Duffing振子的信号检测方法对极微弱周期信号检测有其独到的优势,其频率误差率在控制范围之内。 相似文献
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Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always non-stationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it is flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of IMFs is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures. 相似文献
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《Measurement》2014
Piezoelectric and transient differential pressure sensors are two among the most widely employed sensors for vortex flowmeter application. The present study evaluates the performance of these two techniques under fully developed and disturbed flow conditions. Firstly, the location of the transient differential pressure sensor is optimized to obtain high amplitude signals and good linearity in Strouhal number. Empirical mode decomposition method in combination with autocorrelation decay is successfully employed at high Reynolds numbers to identify the vortex shedding frequency in presence of hydrodynamic noise. The performance of the differential pressure sensor deteriorates significantly under disturbed flow conditions at low Reynolds number due to the presence of low frequency components. This deterioration in the signal quality limits the lower operating range of the flowmeter with differential pressure sensor. The output signals of the piezoelectric sensor and differential pressure sensor under no flow condition are compared to obtain the background noise due to piping vibrations and electrical interferences. These results will help a designer to suggest robust signal processing algorithms for vortex frequency detection. 相似文献