基于扫频技术的乐甫波器件测试系统

介绍了乐甫波器件的结构,设计制作了乐甫波器件,给出了乐甫器件的制作参数,搭建了基于扫频技术的测试系统,包括信号源模块、信号采集模块、微控制器模块等.通过DDS芯片AD9912在一定频率范围内,以一定频率间隔扫频输出高频正弦激励信号,利用AD8302芯片测量扫频范围内每一频率点的器件输入激励信号与输出响应信号幅值比,描绘出幅频特性曲线,并给出器件谐振频率.该测试系统的测量结果与网络分析仪基本一致,实现了网络分析仪的扫频功能.     

组合声源测试消声器传递损失的噪声信号修正方法

针对某些传递损失较大的消声器,现有白噪声测试设备的单一噪声源发生器无法满足消声量的测试需求。通过采用低频和中频两种声源发生器,利用过渡管道与测试管道垂直连接的方式,实现了两种声源组合发声对消声器声学性能的测试。为了解决过渡管道与组合声源系统连接处声阻抗变化,导致输出的噪声信号频谱特性随机波动的问题,提出了一种噪声信号的修正方法。该方法基于四传感器法测量过渡管道声阻抗,根据过渡管道传递矩阵,以随机白噪声为激励源输出的管口噪声作为输出声压信号,得到组合声源系统的输入声压信号,实现了对组合声源系统声音信号的补偿。实验结果表明,与传统均衡器调节方法相比,该方法能够在较宽的测试频率范围内输出平稳的声压信号;其次,利用修正前后的声学信号对扩张腔的传递损失进行测量,修正后得到的测试曲线与理论值吻合度较高,证明了该方法的可行性。     

微光像增强器信噪比测试技术研究

对一台进口的微光像增强器信噪比 (SNR)测试仪进行了改造 ,重新研制和设计了测试仪的信号处理模块、计算机系统和测试软件 ,采用硬件滤波、软件数字滤波和快速傅立叶变换 (FFT)相结合的方法实现了像增强器信噪比测试。利用研制的测试仪对多种型号的像增强器的信噪比进行了测试 ,给出并分析了测试结果。     

水下单边带语音多通道增强技术

船舶噪声是影响水下语音通信质量的主要因素。为了提高单边带语音信号的信噪比和语音质量,采用多通道自适应增强算法对语音信号进行研究。该算法运用自适应信号增强技术,具有运算量小、易实时实现、消噪效果显著等优点,并且能够有效利用不同换能器接收到的信息。从理论上对该算法进行了分析,并结合海试数据进行验证。结果表明,低信噪比、非平稳噪声环境下,该算法有效抑制了接收信号中的尖锐噪声,并且当系统收敛时,使其趋于白化,明显提高了系统的输出信噪比,显著改善了语音质量,在性能上明显优于传统的信号相干叠加算法。     

基于GSO算法的自适应随机共振轴承故障诊断

针对强噪声下轴承故障弱信号较难检测和传统仅靠单参数优化随机共振系统问题,提出一种基于萤火虫优化算法(GSO)的自适应随机共振轴承故障信号检测方法。首先按固定频率压缩比压缩频率;然后以传统随机共振系统输出信噪比作为GSO算法的初始荧光素,利用GSO算法选取随机共振系统的结构参数a、b;最后通过双稳随机共振系统的输出信噪比检测轴承故障弱信号是否增强,通过系统的输出时域图分析信号的周期性,通过功率谱分析轴承故障弱信号的特征频率。仿真验证与试验验证结果分析表明,该方法可检测出轴承故障弱信号,实现弱信号的增强和降噪。     

基于LabVIEW的炉温测控系统设计

首先根据真空热压烧结炉的技术参数和测控系统组成原理,提出两种系统构成方案,分别搭建两套适用于不同要求的硬件系统,并根据具体技术要求进行硬件选型,从信号调理,数据采集,到最后控制输出,构建出两套炉温测控系统的硬件平台,而后利用虚拟仪器开发语言LabVIEW搭建系统的软件平台,采用模块化设计思想,分别设计数据采集模块、测温通道和测温范围设置模块、数据显示分析模块、数据存储模块和温度控制模块,实现对炉温信号的采集、处理、分析、显示和存储。     

基于符号序列熵的自适应随机共振的微弱信号检测

针对现有随机共振以信噪比为测度,难以进行量化和在实际工程中应用,提出一种符号序列改进型香农熵结合随机共振的微弱信号检测方法。介绍了随机共振和符号序列化的基本原理,以输出信号的符号序列熵作为判断是否达到最佳随机共振的测度,自适应的调节系统参数a和b,使系统达到最佳共振并进行频谱分析。结果表明输出信号的符号序列熵可有效反映共振状况,能调节系统达到最佳信噪比输出,而且易于工程实现,证实了该方法的有效性,进而为衡量随机共振提供一种新方法。     

基于 GSO算法的自适应随机共振轴承故障诊断

摘 要:针对强噪声下轴承故障弱信号较难检测和传统仅靠单参数优化随机共振系统问题,提出一种基于萤火虫优化算法(GSO）的自适应随机共振轴承故障信号检测方法。首先按固定频率压缩比压缩频率;然后以传统随机共振系统输出信噪比作为GSO算法的初始荧光素,利用GSO算法选取随机共振系统的结构参数a,b;最后通过双稳随机共振系统的输出信噪比检测轴承故障弱信号是否增强,通过系统的输出时域图分析信号的周期性,通过功率谱分析轴承故障弱信号的特征频率。仿真验证与试验验证结果分析表明,该方法可检测出轴承故障弱信号,实现弱信号的增强和降噪。     

微波、毫米波相位噪声测试系统的研制

提出了首先对微波、毫米波信号进行下变频,再利用锁相环提取被测试信号相位噪声的相位噪声提取方法,采用现代谱分析技术对提取出的相位噪声信号在频率中进行分析,并利用"反卷积"技术实现测试系统的误差校正,研制实现了微波、毫米波相位噪声测试系统.实验测试结果表明该系统具有测试灵敏度高和被测信号频率范围广的优点,证明了它具有较大的应用价值.     

微光像增强器信噪比测试技术研究

对一台进口的微光像增强器信噪比（SNR）测试仪进行了改造，重新研制和设计了测试仪的信号处理模块，计算机系统和测试软件，采用硬件滤波，软件数字滤波和快速傅立叶变换（FFT）相结合的方法实现了像增强器信噪比测试，利用研制的测试仪对多种型号的像增强器的信噪比进行了测试，给出了并分析了测试结果。     

Optimal Estimation of the Acceleration of a Car Under Performance Tests

In this paper, a recursive least-squares lattice (RLSL) adaptive filter was used to carry out the optimal estimation of the relevant signal coming from an accelerometer placed in car under performance tests. Here, the signal of interest is buried in a broadband noise background where we have little knowledge of the noise characteristics. In addition, due to the fact that the noise and the relevant information sometimes share the same or a very similar frequency spectrum, it is very difficult to cancel the noise that corrupts the relevant information without causing that information to deteriorate. The results of the experiment are satisfactory and, in order to compare classical filtering with optimal adaptive filtering, the signal coming from the accelerometer was also filtered by using a third-order lowpass digital Butterworth filter. The results of comparing the aforementioned filters show that the optimal adaptive filter is superior to the classical filter. Here, a significant improvement of 22.4 dB in the signal-to-noise ratio (SNR) at the RLSL adaptive filter output was achieved. However, the improvement in the SNR at the Butterworth filter output was 6.1 dB, which shows very clear that the optimal adaptive filter performs much better than the classical one     

石油管道超声检测信号的两级自适应噪声消除算法

针对石油管道缺陷超声检测信号的噪声消除问题, 研究了一种两级自适应噪声消除算法. 第一级自适应滤波器作为预处理级, 使信号获得较好的相关性和信噪比, 确保第二级自适应滤波器获得更优的性能. 实测超声信号两级自适应滤波结果表明: 两级自适应滤波算法能有效增强超声检测信号中的缺陷信号成分, 显著提高信噪比.     

Spectral histogram using the minimization algorithm-theory and applications to flaw detection

In ultrasonic flaw detection in large grained materials, backscattered grain noise often masks the flaw signal. To enhance the flaw visibility, a frequency diverse statistical filtering technique known as split-spectrum processing has been developed. This technique splits the received wideband signal into an ensemble of narrowband signals exhibiting different signal-to-noise ratios (SNR). Using a minimization algorithm, SNR enhancement can be obtained at the output. The nonlinear properties of the frequency diverse statistic filter are characterized based on the spectral histogram, which is the statistical distribution of the spectral windows selected by the minimization algorithm. The theoretical analysis indicates that the spectral histogram is similar in nature to the Wiener filter transfer function. Therefore, the optimal filter frequency region can be determined adaptively based on the spectral histogram without prior knowledge of the signal and noise spectra.     

Noise robustness of nonlinear filters for image recognition

We analyze the performance of the Fourier plane nonlinear filters in terms of signal-to-noise ratio (SNR). We obtain a range of nonlinearities for which SNR is robust to the variations in input-noise bandwidth. This is shown both by analytical estimates of the SNR for nonlinear filters and by experimental simulations. Specifically, we analyze the SNR when Fourier plane nonlinearity is applied to the input signal. Using the Karhunen-Loève series expansion of the noise process, we obtain precise analytic expressions of the SNR for Fourier plane nonlinear filters in the presence of various types of additive-noise processes. We find a range of nonlinearities that need to be applied that keep the output SNR of the filter stable relative to changes in the noise bandwidth.     

Deconvolution from instrumental devices and source effect inacoustic experiments

Two methods to deconvolve experimental data from the distortions introduced by instrumental devices or source effects are presented. Considering a total acquisition system (emission-reception line, amplifier, pre-amplifier) as a global experimental filter, we can define its properties (module and phase) experimentally from the generation of a family of source signals dilated in time. The estimation of this filter allows the deconvolution of the recorded output signal. The first approach is based on the simple reconstruction formula of the continuous wavelet transform (CWT). The second method is based on the construction of a normalized family of a finite number of specific filters, independent of the frequency range used. In both cases, experimental results in an acoustic tank are presented. We show that after deconvolution, the source signal is correctly reconstructed from the recorded output signal and the global instrumental filter     

随机参数诱导交流积分器中的随机共振研究

研究了具有随机参数的交流积分器中的随机共振现象。基于线性系统理论,得到了系统输出信噪比的表达式。研究发现,输出信噪比是噪声强度、噪声相关率以及系统参数的非单调函数。信噪比随着激励信号频率的增大而增大,随着信号直流分量的增大而减小。     

方波和非对称双值噪声作用下的熵随机共振研究

研究了在非对称双值噪声、白噪声和方波共同作用下受限系统中的熵随机共振现象。在绝热近似条件下,推导出了输出信噪比的表达式。分析表明,信噪比是非对称双值噪声的强度与非对称参数、白噪声强度,以及方波信号幅度的非单调函数。另外,信噪比随受限结构参数的变化而非单调变化。讨论了双值噪声的相关率对SNR的影响。     

基于相控阵激光超声的裂纹衍射增强试验研究

针对超声衍射时差法(TOFD)存在检测精度较差、区域检测可靠性不够和信号信噪比(signal-to-noise ratio, SNR)低等问题,提出了一种基于光纤皮秒激光器和高速旋转镜的相控阵激光超声裂纹检测方法。利用有限元方法模拟热弹机制,建立二维瞬态激光超声力-固耦合模型产生横波与纵波在缺陷处发生的衍射现象,分析了裂纹尖端不同奇异点、声波不同中心频率和相控阵激励源不同位置对声波衍射的影响,通过衍射信号的信噪比和位移幅值两个计算指标来分析变化规律,并进行了试验验证。结果表明:数值模拟与试验结果有较好的一致性,相控阵激光源较传统单束激光源对衍射信号幅值和信噪比有明显的增强作用,纵波衍射信号信噪比较理想;衍射信号幅值随裂纹尖端奇异点增加和声波中心频率减小而增大;信噪比随尖端奇异点增加而增大,随声波中心频率一定范围增加无明显变化,随激光源距离的增加呈现先增加后减小的趋势;缺陷定量分析时计算出的裂纹长度与实际裂纹的误差均不超过6.8%。     

Multiresolution imaging in elastography

The range of strains that can be imaged by any practical elastographic imaging system is inherently limited, and a performance measure is valuable to evaluate these systems from the signal and noise properties of their output images. Such a measure was previously formulated for systems employing cross-correlation based time-delay estimators through the strain filter. While the strain filter predicts the signal-to-noise ratio (SNR(e)) for each tissue strain in the elastogram and provides valuable insights into the nature of image noise, it understated the effects of image resolution (axial resolution, as determined by the cross-correlation window length) on the noise. In this work, the strain filter is modified to study the strain noise at multiple resolutions. The effects of finite window length on signal decorrelation and on the variance of the strain estimator are investigated. Long-duration windows are preferred for improved sensitivity, dynamic range, and SNR(e). However, in this limit the elastogram is degraded due to poor resolution. The results indicate that for nonzero strain, a window length exists at which the variance of strain estimator attains its minima, and consequently the elastographic sensitivity, dynamic range and SNR(e) are strongly affected by the selected window length. Simulation results corroborate the theoretical results, illustrating the presence of a window length where the strain estimation variance is minimized for a given strain value. Multiresolution elastography, where the strain estimate with the highest SNR(e) obtained by processing the pre- and post-compression waveforms at different window lengths is used to generate a composite elastogram and is proposed to improve elastograms. All the objective elastogram parameters (namely: SNR(e), dynamic range, sensitivity and the average elastographic resolution-defined as the cross-correlation window length) are improved with multiresolution elastography when compared to the traditional method of utilizing a single window length to generate the elastogram. Experimental results using a phantom with a hard inclusion illustrates the improvement in elastogram obtained using multiresolution analysis.