相关机械振源的盲源分离方法

相关源不满足独立分量分析关于源的统计独立性假设,标准的独立分量分析方法无法准确分离相关机械振源信号。在相关振源信号的部分频带满足统计独立的假设前提下,提出了一种基于小波包分解的相关机械源盲源分离方法。该方法将观测信号用小波包分解成子带观测信号,根据互信息标准选择相关性较小的若干子带观测信号重构观测信号。通过重构的观测信号的独立分量分析估计分离矩阵,然后用该矩阵分离原始观测信号从而实现相关机械振源信号的分离。仿真试验验证了该方法的有效性。     

基于多个时间点联合解相关的卷积盲源分离

实际信号的混合均为卷积混合,且信号是非平稳的。盲源分离的目标就是找到一组分离滤波器,使得源信号的估计信号互相统计独立。结合信号的非平稳性,利用二阶解相关原理,文章阐明了一种在频域实现卷积混合的盲源分离算法,并且考虑了噪声对分离性能的影响。为了避免频点排列次序的不确定性,利用了多阶段盲源分离思想。利用该算法,对两路混合的实录水声信号进行盲分离,得到了两路源信号的估计信号,通过对估计信号的分析,利用信噪比提高率这一标准,验证了该算法的有效性。该算法收敛速度快,精度高,可用于浅海环境下实录水声混合信号的盲分离。     

运动物体噪声场分解的传递路径声全息方法

声全息及波束成形技术可以通过对阵列噪声信号处理,获得运动物体表面声场的分布。如果物体中声源相距较近或声源相关性较高,该方法不能有效区分每个声源对目标表面声场的贡献。提出了一种基于声源特征的传递路径声全息方法。该方法同时采集外场阵列面信息及物体上声源参考信息,在声源识别过程中,结合声源传递路径对采样面信息分解,然后采用声全息技术重建声源,实现声源识别和声场分解。介绍了该方法的识别过程,并与传统的声全息方法进行多声源仿真对比;最后应用该方法进行了实际汽车噪声源的分解。结果表明,该方法能有效识别并分离出运动物体的噪声源。     

三种干扰噪声对汉语语言清晰度的影响

在不同室内声学特性、不同信噪比条件下探讨三种干扰噪声对汉语语言清晰度的影响。汉语语言清晰度测试信号与干扰噪声信号按照一定的信噪比混合,由听音人进行汉语语言清晰度主观评价。结果表明:当存在干扰噪声时,听音位置的声场特性和信噪比对汉语语言清晰度有显著影响,并且清晰度随听音位置的声场特性的改善和信噪比的提高而提高。听音位置的声场特性和信噪比哪个因素对清晰度的影响更为显著与干扰噪声的频谱分布有关;干扰噪声的频谱分布范围越宽,在相同听音条件(听音位置的声场特性和信噪比)下它对汉语语言清晰度的影响越大。     

Vibration signal modeling of a planetary gear set for tooth crack detection

In a planetary gearbox, there are multiple vibration sources, and the transmission path of vibration signals changes due to the rotation of the carrier. This study aims to model the vibration signals of a planetary gearbox and investigate the vibration properties in the healthy condition and in the cracked tooth condition. A dynamic model is developed to simulate the vibration source signals. A modified Hamming function is proposed to represent the effect of the transmission path. By incorporating the effect of multiple vibration sources and the effect of transmission path, resultant vibration signals of a planetary gearbox are obtained. Through analyzing the resultant vibration signals, some vibration properties of a planetary gearbox are revealed and the fault symptoms of sun gear tooth crack are identified and located. Finally, the proposed approach is experimentally verified.     

振动信号时变特征的提取,压缩及机械状态监测

往复式机械和许多周期运转机械的振动激励都具有脉冲性质,振动响应的时间相关局部细节包含丰富的故障信息.本文提出了振动信号的时变模型,用以提取机械运转特征,并提出使用正交变换描述特征母体的一阶矩和二阶矩的方法,在时变特征提取、压缩的基础上,引入模式识别中的距离准则,判断任一信号与母体的相似性,以作为一种监测的度量手段.一个关于柱塞泵振动信号监测的例子说明了本方法的使用过程.     

Identifying the signal of interest for partially overlapping ultrasonic echoes

The basic problem addressed in this paper is to discriminate between two signals that are at approximately the same time, but which originate at different echo sources. The proposed solution is to systematically perturb the field and discriminate between signals based on differences in amplitude variations between the two signals.     

Multiscale ICA for fundus image analysis

Independent component analysis is a technique used for separation of statistically independent sources. It can estimate unknown sources from a mixture of sources without any prior knowledge about them. The sources should be non‐Gaussian and independent with each other. In this work, multiscale ICA is proposed for medical images (fundus images, MRI Images). The data matrix is formed by considering the higher sub‐bands of multiscale decompositions. Performance of multiscale ICA is evaluated and compared with the ICA algorithms using simulated signals and different medical images using Amari performance index and Comon test values. Results show that API and Comon test values are less for multiscale ICA for simulated signals. In case of pathological images, the features are separated correctly by multiscale ICA. Multiscale ICA performs better than simple ICA for separation and detection of independent components from medical images (fundus images), such as blood vessels and artifacts. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 327–337, 2013     

基于自相关降噪的混叠转子振动信号分离

航空发动机在运行过程中,传感器测得的振动信号是各振源的混叠信号,且含有很强的噪声。常规的信号处理方法难以分离混叠信号,对机器的健康监测和故障诊断造成了很大的困难。介绍了盲源分离基本原理和方法,指出盲源分离算法在强噪声环境下失效。针对强噪声环境下的混叠振动信号,提出首先通过时延自相关降噪方法对振动信号进行降噪,然后通过盲源分离算法对降噪后的信号分离。仿真结果验证了提出方法的有效性。最后,利用该方法对实测混叠转子振动信号成功实现了降噪和盲分离,为噪声环境下的混叠信号分离提供了一种新的方法。     

The signal/noise ratio at the output of a correlometer when measuring the variable delay of steady signals

The problems of estimating and increasing the signal/noise ratio at the output of a correlation delay measuring instrument in the passive location of moving sources of steady signals are considered. A procedure for determining the optimum integration interval when constructing the cross-correlation function of signals from such sources is proposed, which ensures the maximum signal/noise ratio. __________ Translated from Izmeritel’naya Tekhnika, No. 9, pp. 49–54, September, 2006.     

基于振源识别的高速微铣削机床状态研究

对介观尺度下三向正交的高速微铣削力混合信号提出一种有效的分离算法,从而获得高速微铣削力的真实信号及机床振动信号并基于此对机床状态进行分析。算法首先以信息量为处理标准将观测信号初步分离,再将分离得到的各分量信号作为行向量构造矩阵,最后对该矩阵进行以高斯性最大为度量的分离,逐一得到各激励源信号,并对其快速傅里叶变换得到频谱,结合机床结构及介观尺度下高速微铣削加工特点,识别微铣削力信号及机床状态信息。实验结果分析表明：该方法可成功对介观尺度下高速微铣削加工中主要激励源和机床状态进行有效识别。     

An Electrically-Driven GaAs Nanowire Surface Plasmon Source

Over the past decade, the properties of plasmonic waveguides have extensively been studied as key elements in important applications that include biosensors, optical communication systems, quantum plasmonics, plasmonic logic, and quantum-cascade lasers. Whereas their guiding properties are by now fairly well-understood, practical implementation in chipscale systems is hampered by the lack of convenient electrical excitation schemes. Recently, a variety of surface plasmon lasers have been realized, but they have not yet been waveguide-coupled. Planar incoherent plasmonic sources have recently been coupled to plasmonic guides but routing of plasmonic signals requires coupling to linear waveguides. Here, we present an experimental demonstration of electrically driven GaAs nanowire light sources integrated with plasmonic nanostrip waveguides with a physical cross-section of 0.08λ(2). The excitation and waveguiding of surface plasmon-polaritons (SPPs) is experimentally demonstrated and analyzed with the help of full-field electromagnetic simulations. Splitting and routing of the electrically generated SPP signals around 90° bends are also shown. The realization of integrated plasmon sources greatly increases the applicability range of plasmonic waveguides and routing elements.     

用两种软件对比分析四缸发动机振源

针对某直列四缸发动机,利用MATLAB和ADAMS两种软件分别对其进行振源分析。首先通过理论分析初步确定该机的主要激振源:二阶往复惯性力和倾覆力矩,利用MATLAB软件对激振源的时频域特性进行研究。然后应用ADAMS软件对该发动机进行动力学仿真,在后处理中选择适当位置的测量信息,经过进一步分析确定该机的激振源类型及其时频域特性。将用ADAMS分析得到的结果与MATLAB分析得到的结果进行对比,其一致性良好,从而验证两种分析方法的正确性。分析结果为进一步对该发动机进行振源控制提供必要的数据参考。     

基于粒子群算法优化的独立分量分析算法

通过两组模拟信号对三种主流独立分量分析算法-JADE、FastICA、扩展Infomax算法的性能进行了对比分析,结果表明三种算法均无法完全分离超高斯源与亚高斯源形成的混合信号,FastICA算法对能量强弱差别大的混合信号失效。基于这一现象,提出了一种新的独立分量分析算法,以粒子群算法为优化工具,以分离矩阵为优化变量,最小化分离信号联合概率与边缘概率乘积的差值,并给出了具体的计算流程。仿真实验结果表明,该算法的性能显著优于上述三种独立分量分析算法。同时,新提出算法实施过程中不需要任何先验知识,相比其他三种ICA算法,更适合解决工程实际问题。最后,将该算法应用于对滚动轴承实验台实测信号的处理,通过对分离信号的分析实现了对滚动轴承故障类型的准确识别,进一步证明了算法的有效性。     

室内声学测量中常用声源性能的比较

在室内声学测量中，参数的测量结果受到测试技术和系统的影响。从测试技术来看，传统的方法是使用模拟信号源(气球、发令枪等)，而现代的方法是使用计算机产生的数字的信号，通过扬声器发出。本文扼要阐述了室内声学传统的测量方法，对气球破裂，电火花及白噪声等三种常用声源的瞬态时频特性及其典型实用测量作了对比分析研究，得出电火花频谱较宽，且较接近于理想的白噪声谱，信号的重复性也较好，是其中最佳的声源。但这些传统测量方法的共同缺点就是信号不能完全重复，测量精度不够，一般只适用于混响较长的房间的测量，文中对它们的适用范围及其局限性作了讨论，为进一步研究工作提供了基础。     

A calibration approach to acoustic emission energy measurement

An approximate method for the quantitative characterization of acoustic emission sources whose signals are recorded with traditional instrumentation has been developed to evaluate the emission from a Fe-Ni alloy undergoing martensitic transformations. The calibration principle is based upon using pseudo sources of known elastic strain energy to produce a calibration curve relating a parameter of the acoustic emission signal measured (envelope strength) to the strain energy of the source. The curve can then be used to determine the strain energy of naturally occurring sources during martensitic transformations.     

机械声混合及其盲分离技术的研究

盲源分离是一个很有优势的盲信号分析与处理工具,在机械状态监测与故障诊断领域有较好的应用前景.但在实际应用于机械源信号、尤其是机械声源信号分离中,源分离精度还不够高.鉴于此,在分析机械声产生、传播与混合机理以及盲分离算法基础上,提出了新的解决途径,特别是基于带通滤波改进的盲源分离新方法.仿真以及实际的机械声源盲分离实验结果,证实了新方法的有效性.     

一般阵列误差情况下信号二维方向角和多普勒频率的联合估计

针对平面阵列，推导出基于一般阵列误差模型的信号时空DOA矩阵，采用总体最小二乘法（TLS）估计出多个信号的方位角。俯仰角和多普勒频率，解决了大多数估计算法的性能因阵列误差而严重下降和“频率兼并”问题。此算法对噪声不敏感，不需进行谱峰搜索，仿真结果表明了此算法的有效性。     

基于聚类分析的罐底声发射检测信号融合方法

采用声发射技术进行罐底腐蚀与泄漏检测过程中,需要对多个传感器的检测到的声发射信号进行融合处理,将属于同一声发射源的声发射信号判定为一个声发射事件。但是在现场检测过程中,由于噪声的存在,使得在声发射信号融合处理时容易对声发射源产生误判。针对该问题,提出了一种基于聚类分析的罐底声发射信号融合方法,其基本原理是先根据事件定义时间进行初始声发射事件判定,然后采用聚类分析方法对初始声发射事件中的信号进行分类,将每一类信号分别判定为一个声发射源。现场实验表明采用该方法抗噪声干扰能力强、误判概率低,能准确反应罐底腐蚀的实际情况。