能量算子解调方法及其在机械信号解调中的应用

总结了能量算子法的解调原理,与作为标准解调法的希尔伯特变换法作了比较,探索了它在机械信号解调中的运用。研究发现,能量算子方法是一种很好的解调方法,在机械信号的解调分析中大有可为。     

遥测系统的信号解调及研制

研究了遥测系统中数字信号的检波和解调,介绍了以PLC为核心的遥测网络系统的相关设计, 讨论了该系统的硬件和软件的研制。     

基于数据采集的调幅信号软件解调方法

提出了在智能仪表中对传感器输出调幅信号实现解调的一种新方法。借鉴半波相敏检波的基本原理，采用载波信号作为微机的中断触发信号，采集调幅信号，可直接获得调制信号幅值离散点的数字量，在实现解调的同时完成信号测量。给出了用微机实现信号解调的基本原理、基于单片机AT89C2051设计的硬件接口电路以及软件设计的基本思路。     

光纤光栅传感系统信号解调技术的研究

实现波长编码信号的解调，是光纤光栅传感系统实用化推广的关键技术之一。文中讨论了光纤光栅传感信号解调的基本原理，分析了国内外提出的常用解调方法的工作机理、特点和性能，并总结了信号解调过程中存在的主要技术难点及发展方向，为基于光纤光栅传感系统的解调部分的设计提供了依据。     

齿轮箱故障诊断中信号解调方法的研究

提出了一种对齿轮箱调制类故障信号进行诊断的综合方法，对于既有调幅成分又有调频成分的齿轮箱故障信号，提出首先利用Hilbert变换解调出包络信息，同时保留剩余信号，然后用三次FFT技术对调频信息进行解调，使得调频信号特征更加清晰，为齿轮箱故障诊断提供了更准确的信息。     

多极旋转变压器信号的软件解调实现

针对多极旋转变压器信号采用软件进行解调实现时遇到的四个问题:相位漂移、噪声干扰、相位调整和实时性,给出了详细的分析和有效的解决办法,具有良好的工程应用价值。     

基于小波包的全信息解调方法及其应用

针对旋转机械故障信号的振动特点,将小波包络解调与基于数据融合技术的全矢谱相结合,提出一种诊断旋转机械调制信号的分析方法。首先,对安装在转子同一截面不同方向上的传感器信息同步整周期采样,对来自不同方向的时域信号分别采用小波包进行分解并重构,以实现带通滤波的效果;然后,采用全矢谱技术对两组重构信号进行数据融合;最后,对合成后的信号做包络解调分析。通过仿真研究和工程实例分析可以得出,对来自同一截面、不同方向的时域信号分别作小波包络谱分析时,两者在能量分布和频谱结构上存在着较大差别,以致造成提取故障信息的不完整或造成误判、漏判。基于小波包的全信息解调分析方法通过对同源的双通道信号的有效融合,可全面地反映出信号中包含的不同调制信息。与基于全矢谱的传统包络解调分析进行对比分析,具有较好的分析结果和可信度。     

轨道电路交流计数信号的解调

随着轨道的发展和列车速度的提高,对轨道信号的识别、解调有了更高的要求。交流计数电码自动闭塞是我国轨道电路上应用较多的一种闭塞制式,对其进行精确的解调具有非常大的意义。本文提出了Hilbert变换和小波变换组合的新方法并对真实信号进行了解调。1轨道电路交流计数信号交流计数是以25Hz、75Hz(电力牵引区段)和50Hz(非电力牵引区段)为载频的调幅信号,调幅信号的周期为1.92s(长码)或1.6s(短码),采用键控脉冲的数目区分电码信号:一个周期内3个脉冲为绿码,2个脉冲为黄码,1个脉冲为红黄码。其码型如图1所示(短码为例)。当前较成熟的解调方…     

Using a stochastic basis in signal and image recovery problems

This paper touches upon the problems of constructing a basis for stochastic functions, which represent samples of random numbers with a given distribution law. The algorithm for constructing an approximation model with the use of this basis is described. The general algorithm for signal recovery using the stochastic basis by the least squares method with a given weight function is given. The approach to solving the problem of recovery of blurred images with a known point scattering function by constructing an inverse filter model is proposed.     

Invited article: Linearization and signal recovery in photoacoustic infrared spectroscopy

Photoacoustic (PA) infrared spectroscopy enables the characterization of a wide variety of materials, affording the spectroscopist several advantages over more traditional infrared methods. While PA spectra are readily acquired using commercial instrumentation, the quality of the data can be improved substantially through the use of specialized numerical and experimental procedures. Two of these methods are the subject of this review. Specifically, this article describes (a) linearization of PA infrared spectra, a calculation that incorporates phase and amplitude information to extend the range of linearity for strongly absorbing samples, and (b) lock-in and digital signal-recovery procedures in step-scan phase-modulation PA infrared spectroscopy. Linearization yields significant improvement in band definition, especially in the low-wavenumber region. This numerical method succeeds in situations where the PA phase of the sample is less than that of the reference (carbon black). When this criterion is not met initially, the sample or reference interferograms can be manipulated prior to the calculation. The steps involved in linearization are illustrated in detail and approximations are discussed. Lock-in demodulation of the step-scan phase-modulation signal is compared to digital (software) demodulation in this study; the lock-in technique is found to be superior in several cases. The imaginary interferograms in these experiments sometimes lack a strong central feature, a situation that necessitates the application of less commonly used methods for phase correction and spectrum calculation. These methods, which are available in commercial software, include two-quadrant and stored-phase corrections. The PA phase spectrum resembles amplitude and absorption spectra when real and imaginary PA spectra are correctly calculated.     

Measuring information signal parameters under additive non-Gaussian correlated noise

Issues related to measuring the information parameters of signals reflected from an object under correlated additive non-Gaussian noise are considered. It is shown that in the presence of correlated non-Gaussian noise, increasing the correlation coefficient increases the generalized signal/noise ratio, which, in turn, improves the measurement accuracy of signal parameters. The obtained dependences confirm that the measurement error of signal information parameters is affected not only by the generalized signal/noise ratio, but also by accounting for the non-Gaussian nature of the additive noise, which leads to a significant improvement in the measurement accuracy of these parameters.     

悬浮颗粒物信号幅度分布提取及信息熵的应用

为精确反演悬浮颗粒物的质量浓度,对颗粒物信号幅度分布的提取进行了研究。提出了线性和非线性分割两种信息提取方法,比较了两种提取方法得到的质量浓度的反演精度和稳定性。实验结果表明,非线性分割法的反演精度稳定较快,即需分割的信号子集数较少;当信号子集数大于32时,两种提取方法的反演精度均达到稳定且趋于一致。通过计算信号幅度分布的信息熵,发现信号子集数相同时非线性分割法提取信息的信息熵明显较大,表明信号幅度分布的信息熵大小决定了质量浓度反演精度的稳定速度。信号幅度分布提取方法的研究为粒子计数器系统的电路设计提供了理论指导。     

Levy噪声下一阶线性系统的弱信号复原分析

将Levy噪声与一阶线性随机共振SR系统相结合,采用Levy噪声模型和弱正弦信号模型,研究了不同Levy噪声环境下的调参广义随机共振现象及弱信号复原。首先分析了Levy噪声的特征指数α、对称参数β以及强度系数D对输入信噪比的作用规律;然后探究了不同分布的Levy噪声环境下一阶线性系统结构参数a的广义随机共振现象;最后提出了Levy噪声激励下线性系统高低频弱信号复原方法;研究结果表明:输入信噪比随α单调递增,随μ变化甚微,随D单调递减到一定程度后,不再减小保持定值;在Levy噪声作用下的一阶线性系统不能产生传统意义上的随机共振现象,但却存在互相关系数随结构参数a非单调变化的广义随机共振现象;在信号复原过程中,理论分析与实际仿真结果一致,证明所提复原方法准确可行,复原效果理想     

An improved stopping condition guarantee recovery of sparse signal via Subspace Pursuit method

The Subspace Pursuit (SP) algorithm is one of greedy pursuit methods which is used to reconstruct of K-sparse signal. Unlike existing condition produced by Dai and Milenkovic in 2004 that suggests the residual value of current iteration is reduced from the previous iteration, our approach eliminates useless information by reducing the number of iterations used to detect the correct support set. This operation is done by suggesting a new halting condition that can capture the best support set which can give the best representation of the reconstructed signal. The new halting conditions enhanced the SP algorithm to low computational complexity and reconstruction accuracy of the sparse signal.A mathematically proven for two halt condition: noiseless setting, and noisy setting for signal affected by Gaussian noise. An error bound relation also is driven.In this paper, we try also to relax the restricted isometry constant RIC value to narrows the gap between the known bounds and ultimate performance, which it produced by Dai.Simulation results show that the new halting condition can overpass best results produce by earlier iteration and rise time consume. Our new halting condition can catch this earlier iteration and enhanced SP algorithm results.     

基于奇异值分解的压缩感知噪声信号重构算法

压缩感知是通过对信号信息采样的信号处理新方法,它对可压缩信号可以大大降低采样数据.为提高噪声信号在压缩感知中的重构精度,本文提出了一种基于对观测矩阵奇异值分解的噪声信号重构算法,该算法对随机观测矩阵进行奇异值分解,通过均值算法修改对角矩阵的特征值,产生新的观测矩阵用于线性测量,理论证明了新观测矩阵比原观测矩阵具有更高的重构精度.仿真结果表明,算法重构精度在一维信号提高了3％～5％,二维信号的PSNR值提高1～2 dB.     

基于EEMD和HT的轴流泵压力脉动特征信息提取

压力脉动是影响轴流泵运行稳定性的重要因素,为提取其压力脉动信号中的特征信息,提出了采用基于聚合经验模式分解(EEMD)和Hilbert变换(HT)的时频分析方法对轴流泵压力脉动信号进行分析。首先分别应用EEMD和传统经验模式分解(EMD)对含噪声信号进行了分析,证明了EEMD分解能抑制传统EMD中出现的模式混叠现象,从而有效提取了信号中的各频率分量;然后采用基于EEMD和Hilbert变换的时频分析方法,对某轴流泵的压力脉动信号进行了分析。研究结果表明,该方法能够准确地提取轴流泵压力脉动信号中的频率成分及其时变情况。