一种自适应前向均衡与判决均衡组合结构及变步长改进算法

信号在超长线缆传输中, 线缆的线间串扰及温度梯度变化造成噪声干扰, 特别是线缆介电损耗和肌肤效应的影响, 导致接收端信号频率色散失真严重, 难以恢复原始信号, 限制了传输速度. 另外, 在页岩气、煤层气等资源勘探领域, 在用长缆传输数据时, 还要求传输高精度同步脉冲信号, 用于采集数据相位的标定. 线缆的传输效应及噪声干扰严重影响了接收端的信号同步, 造成采集数据相位失真. 本文针对信号在长缆传输中的非线性失真及衰减问题, 提出了一种新型均衡结构, 并针对新模型给出了最优系数组合. 在此基础上针对改进的结构提出了一种基于反正切函数的变步长算法, 该算法配合三误差因子, 形成收敛函数, 该函数具有收敛速度快, 稳态误差小的优点. 改进后的自适应组合均衡器计算复杂度低, 收敛快, 信道跟踪能力强, 加快了数据处理速度, 同时能较好地应对信道的时变性. 仿真结果表明, 基于新变步长算法的自适应组合均衡器, 性能上提高了50%, 并且消除了噪声干扰和码间干扰, 测试实验表明, 在无中继超长缆(7 km以上)传输中, 信号速度提高了一倍.     

基于近邻轨道平均长度的混沌时间序列分类方法

在混沌时间序列的复原图（recurrence plot, RP）基础上，提出了两混沌时间序列的相干复原图（cross recurrence plot, CRP）的定义，并在CRP上提取出两时间序列在同一重构相空间中演化趋势接近的轨道的平均长度（近邻轨道平均长度），最终实现对混沌时间序列的分类.通过对Lorenz信号的分析，得出两时间序列的近邻轨道平均长度随着它们的重构轨道在相空间中的演化相似性的减小而减短；通过本方法与Schreiber提出的相干预测误差分类方法对心电信号（ECG）的节律变化的检测实验，得 关键词： 时间序列分类 复原图 相干复原图 近邻轨道平均长度 相干预测误差     

基于平稳小波和相空间重构的激光混沌预测

提出了一种激光混沌时间序列预测算法.该算法通过平稳小波分解，将原始数据序列分解为与原序列等长的尺度系数和小波系数，利用坐标延迟理论，重建各级尺度系数和各级小波系数的相空间,再根据混沌吸引子的稳定性和分形性,在相空间中对尺度系数和小波系数进行预测,进而通过平稳小波重构算法,实现了时间序列的非线性预测.该算法对数据可以进行更平滑的处理，比无小波算法预测的时间范围更长.通过仿真试验说明，原始时间数据序列被成功的重建，说明算法能够有效的对非线性动态系统的时间序列进行建模和预测.     

基于滑模变结构控制及预测跟踪的光电吊舱控制方法北大核心CSCD

光电吊舱在稳像和目标跟踪过程中存在摩擦力矩、不平衡力矩等干扰力矩,从而影响速度环响应精度;另一方面,吊舱视频跟踪器图像传输和处理造成的延迟也会造成跟踪滞后,因此必须进行延时补偿。提出基于预测跟踪的滑模变结构控制方法,采用微分预测跟踪器实现对视频跟踪器的延迟补偿,采用预测跟踪器估计出的目标运动角速率构成自适应补偿参数,以调整滑模变结构控制量,改进的滑模控制算法在补偿干扰力矩的同时抑制了抖振现象。仿真及实验结果表明:改进的控制方法能够有效补偿干扰力矩和跟踪器延迟造成的误差,相对于传统PID控制,其跟踪误差减小为原来的1/3,并且该算法已经在相关系统上得到应用。     

稀疏正交联合约束多通道非负矩阵分解声信号分离算法

针对复杂环境下多通道声信号分离问题,提出稀疏正交联合约束多通道非负矩阵分解声信号分离方法。首先设计基于多通道扩展坂仓斋藤(Itakura-Saito,IS)散度的稀疏正交联合约束项构造代价函数,给出信号稀疏和信号正交约束辅助函数,实现代价函数最小化求解。然后通过迭代更新规则设计,得到稀疏正交优化的多通道非负矩阵分解基矩阵和系数矩阵,讨论了稀疏正交约束对基矩阵和系数矩阵稀疏性与连续性影响。最后基于多通道信号空间特性,进行了非负矩阵分解基聚类以获得多通道非负矩阵分解声信号的分离结果。双通道音频数据与四通道声学目标分离实验数据测试表明,对音频数据,所提算法在性能指标信号失真比(SDR)上提高了0.84 dB,对于直升机声源数据,所提算法在SDR上提高了4.53 dB。     

基于弹性变分模态分解的癫痫脑电信号分类方法

癫痫脑电信号分类对于癫痫诊治具有重要意义.为了实现病灶性与非病灶性癫痫脑电信号的分类,本文利用弹性网回归重构变分模态分解算法,提出弹性变分模态分解算法并将其应用到所提癫痫脑电信号分类方法中.该方法先将原信号分割成多个子信号,并对各子信号进行弹性变分模态分解,然后从分解后的不同变分模态函数中提取精细复合多尺度散布熵作为特征,最后利用支持向量机进行分类.针对癫痫脑电的公共数据集,最终的实验结果表明,准确率、灵敏度和特异度三个性能指标分别达到92.54%,93.22%和91.86%.     

语音信号序列的Volterra预测模型

对给定的英语音素、单词和语句进行了采集并完成预处理. 分别应用互信息法和Cao 氏法确定了实际采集的语音信号序列的延迟时间和嵌入维数, 以完成语音序列的相空间重构. 通过计算实际采集的语音信号序列的最大Lyapunov指数, 完成了语音信号的混沌特性识别, 判定其具有混沌特性. 引入Volterra级数, 提出了一种具有显式结构的语音信号非线性预测模型. 为克服最小均方误差算法在Volterra模型系数更新时固有的缺点, 在最小二乘法基础上, 应用基于后验误差假设的可变收敛因子技术, 构建了一种基于Davidon-Fletcher-Powell算法的二阶Volterra 模型(DFPSOVF), 并将其应用于具有混沌特性的语音信号序列预测. 仿真结果表明: DFPSOVF非线性预测模型对于单帧和多帧语音信号均具有更好的预测精度, 优于线性预测模型, 并且能够很好地反映语音序列变化的趋势和规律, 完全可以满足语音预测的要求; 可以根据语音信号序列的嵌入维数选取预测模型的记忆长度. 所提出模型可以为语音信号重构和压缩编码开辟一条新途径, 以改善语音信号处理方法的复杂度和处理效果.     

基于LPV的航空发动机鲁棒变增益控制

随着我国现代化进程的不断加快,航天航空技术标准越来越高,对于航空发动机运转工况的鲁棒性和适应性提出了更高的要求。传统的航空发动机变增益设计步骤繁琐,不能将发动机置于整个航空器的运转去考虑设计,使发动机变增益缺乏相应的稳定性和适应性,易出现系统问题。为此,提出一般基于LPV的航空发动机鲁棒变增益控制系统,依据航空发动机结构参数,考虑到航空器在空中负载特性,计算出新的约束极点 模糊变增益,在航空器发动机工作范围连续增益,避免了传统增益切换情况,在转速控制上确定误差等因素,将非线性控制设计分解为多个线性子问题,使航空器控制系统能够沿着LPV参数轨迹保持良好的运转,保持稳定性能。仿真实验证明,提出的基于LPV的航空发动机鲁棒变增益控制系统控制效果优于传统方法,在航空器发动机转速改变时,控制精度能够满足要求 ,改变航空器负载时,有效对目标进行变增益控制。提出的控制方法对航空发动机鲁棒变增益控制问题提供了新的解决办法,具有较大应用价值。     

基于改进变分模态分解的北极海域声速剖面分类*

应用支持向量机对北极声速剖面进行分类,特征量提取是关键。该文采用一种基于经验模态分解的改进变分模态分解算法,以准确提取声速剖面特征量。算法首先对声速剖面信号进行经验模态分解,依据最大类间方差原则划分各分量边际谱主频带,以相似度作为最小分解层数判断标准,获得最小分解层数,进行变分模态分解。对北极区海水声速实测数据(信号)处理表明,该方法可有效提取信号经验模态分解各分量的希尔伯特边际谱特征,进行支持向量机分类,实现对北极海域声速剖面的分类识别,解决以往人工分类耗时久的问题。     

变尺度概率净化法的优化

变尺度概率净化法是一种混沌时间序列非线性动力学降噪方法，该方法需对整个相空间点列做联合处理，因此算法的计算量和所需内存量会随着每个轨道点的修正参考点数和嵌入维数的增加而呈指数增长.根据变尺度概率净化法的特点，对前向概率和转移概率的估计方法作了一些改进，使算法的运算量减小到了原来的0.27左右，而降噪性能并没有下降，并提出了数据较长情况下的算法实现结构，大大降低了算法运行所需内存. 关键词： 混沌 非线性动力学 降噪 最大后验概率估计 变尺度概率净化     

基于新的五维多环多翼超混沌系统的图像加密算法

本文提出了一种基于新的五维多环多翼超混沌系统的数字图像加密方法.首先,将明文图像矩阵和五条混沌序列分别通过QR分解法分解成一个正交矩阵和一个上三角矩阵,将混沌系统产生的五条混沌序列分别通过LU分解法分解成一个上三角矩阵和一个下三角矩阵,分别将两个上三角矩阵和一个下三角矩阵相加,得到五个离散后的混沌序列;其次,将明文图像矩阵分解出来的正交矩阵与五个混沌序列分解出来的五个正交矩阵相乘,同时把明文图像矩阵分解出来的上三角矩阵中的元素通过混沌序列进行位置乱,再将操作后的两个矩阵相乘;最后,将相乘后的矩阵通过混沌序列进行比特位位置乱,再用混沌序列与其进行按位“异或”运算,得到最终加密图像.理论分析和仿真实验结果表明该算法的密钥空间远大于10^200,密钥敏感性强,能够有效地抵御统计分析和灰度值分析的攻击,对数字图像的加密具有很好的加密效果.     

THE APPLICATION OF PSEUDO-PHASE PORTRAIT IN MACHINE CONDITION MONITORING

This paper presents a robust phase space reconstruction method based on singular value decomposition technique and its applications to large rotating machine and gear system condition monitoring and fault diagnosis. The singular value decomposition is used to determine the effective embedding space and to reduce the noise level of a measured vibration signal. Following the singular value decomposition, a pseudo-phase portrait can be obtained in the effective embedding space. This pseudo-phase portrait is then used to extract qualitative features of machine faults. Experience has shown that when one compares the pseudo-phase portraits obtained under different machine conditions, it is often possible to detect major differences due to different dynamic and kinematic mechanisms. In the case of gear system condition monitoring, correlation dimension has been introduced to evaluate these differences in order to obtain more accurate and reliable diagnosis. The pseudo-phase portrait is conceptually simple and has been found to be sensitive to some fault types. It is promising therefore that such pseudo-phase portraits can be used to realize real-time, online computer-aided diagnosis of machine faults.     

Envelope order tracking for fault detection in rolling element bearings

An envelope order tracking analysis scheme is proposed in the paper for the fault detection of rolling element bearing (REB) under varying-speed running condition. The developed method takes the advantages of order tracking, envelope analysis and spectral kurtosis. The fast kurtogram algorithm is utilized to obtain both optimal center frequency and bandwidth of the band-pass filter based on the maximum spectral kurtosis. The envelope containing vibration features of the incipient REB fault can be extracted adaptively. The envelope is re-sampled by the even-angle sampling scheme, and thus the non-stationary signal in the time domain is represented as a quasi-stationary signal in the angular domain. As a result, the frequency-smear problem can be eliminated in order spectrum and the fault diagnosis of REB in the varying-speed running condition of the rotating machinery is achieved. Experiments are conducted to verify the validity of the proposed method.     

旋转机械振动故障诊断专家系统

针对旋转机械故障诊断智能化程度不高的现状,采用可视化语言LabVIEW作为故障诊断专家系统前端开发平台,采用MySQL作为系统后台数据库,开发了一个基于产生式规则表示知识的旋转机械故障诊断专家系统。介绍了该系统的总体结构、知识库、推理机的设计。最后给出该专家系统的实例验证,证明了该方法的实用性。     

Analysis on accuracy improvement of rotor–stator rubbing localization based on acoustic emission beamforming method

This paper attempts to introduce an improved acoustic emission (AE) beamforming method to localize rotor–stator rubbing fault in rotating machinery. To investigate the propagation characteristics of acoustic emission signals in casing shell plate of rotating machinery, the plate wave theory is used in a thin plate. A simulation is conducted and its result shows the localization accuracy of beamforming depends on multi-mode, dispersion, velocity and array dimension. In order to reduce the effect of propagation characteristics on the source localization, an AE signal pre-process method is introduced by combining plate wave theory and wavelet packet transform. And the revised localization velocity to reduce effect of array size is presented. The accuracy of rubbing localization based on beamforming and the improved method of present paper are compared by the rubbing test carried on a test table of rotating machinery. The results indicate that the improved method can localize rub fault effectively.     

基于主元分析与动态时间弯曲的故障诊断方法及应用研究

轴承是工程实际中常用而又极易损坏的部件,特别是对其早期微弱响应的辨识,具有重要的社会价值和意义。为提高运转轴承的安全可靠性和可维护性,提出了基于主元分析与动态时间弯曲距离的故障诊断方法,它可以准确对早期微弱动态响应辨识、诊断。该方法首先将典型故障样本信号与待测信号小波去噪并EMD分解,并对若干固有模态分量主元分析求取主元,然后对主元分量进行分析,获得相关特征值组成特征向量,计算待测信号与已知故障样本信号特征向量的弯曲距离,弯曲距离越小表明两信号越相似,从而辨识故障。此外,还可将其应用于转子、碰磨、齿轮故障诊断中,工程应用实例表明该方法可以准确故障分类,高效故障诊断。     

Noise rejection for two time-based multi-output modal analysis techniques

Modal analysis is a well-developed field with many applications. In particular, forced response multi-output approaches are well suited for system identification and online damage detection because they use the natural excitations the system undergoes during its normal operation. In this work, two of these approaches, smooth orthogonal decomposition (SOD) and direct system parameter identification (DSPI), are analyzed, compared, and improved upon. SOD was originally developed as a tool for detecting features of chaotic dynamical systems. Recently, it has been used as a time-based multi-output modal analysis approach. SOD has been demonstrated to work for the free vibration case and for random excitations. DSPI was developed as a time-based multi-input multi-output approach. When the inputs are not measured, DSPI is very similar to SOD and can handle both free vibrations and random excitations. However, if the inputs are measured or known DSPI can also handle arbitrary excitations. To improve the performance of these two methods when used with noisy data, novel noise filtering algorithms are proposed. Numerical simulations are performed to compare the two methods and to show the effectiveness of the filtering algorithms in improving frequency and mode shape extraction.     

A multiscale noise tuning stochastic resonance for fault diagnosis in rolling element bearings

Condition monitoring of rotating machinery is important to extend the mechanical system's reliability and operational life. However, in many cases, useful information is often overwhelmed by strong background noise and the defect frequency is difficult to be extracted. Stochastic resonance (SR) is used as a noise-assisted tool to amplify weak signals in nonlinear systems, which can detect weak signals of interest submerged in the noise. The multiscale noise tuning SR (MSTSR), which is originally based on discrete wavelet transform (DWT), has been applied to identify the fault characteristics and has also increased the signal-to-noise ratio (SNR) improvement of SR. Therefore, a novel tri-stable SR method with multiscale noise tuning (MST) is proposed to extract fault signatures for fault diagnosis of rotating machinery. The wavelet packets transform (WPT) based MST can obtain better denoising effect and higher SNR of resonance output compared with the traditional SR method. Thus the proposed method is well-suited for enhancement of rotating machine fault identification, whose effectiveness has been verified by means of practical vibration signals carrying fault information from bearings. Finally, it can be concluded that the proposed method has practical value in engineering.     

基于遗传神经网络的旋转机械故障预测方法研究

许多大型旋转机械运行工况恶劣,非平稳、非线性特征明显,以及各种突发性、偶然性因素的影响,给基于振动信号处理的状态预测和状态维护分析带来困难。神经网络以其强大的处理非线性系统的能力在故障预测中得到广泛的应用,但由于其在追求高精度训练目标时易陷入局部极值,且收敛速度慢甚至发散。针对这个问题,提出了采用遗传算法对神经网络连接权值和阈值进行优化,这样不仅发挥了神经网络广泛的映射特性也使遗传算法的全局搜索优势尽显无疑。通过组合这两种算法,在提升网络学习的准确度方面,优点尤其突出,最终提高对旋转机械故障预测和寿命估计的性能,这在某环境模拟试验系统动力风机的轴承磨损故障预测中得到了验证。     

三轴光电跟踪系统对地平式天顶盲区出入点的判定方法

三轴光电跟踪系统可以全方位跟踪空间目标,在地平式光电跟踪系统天顶盲区内通过转动X轴实现快速高精度跟踪。用多种三轴光电跟踪系统对地平式天顶盲区进行判定,包括目标距离信息判定方式、Z轴转动速度判定方法、预置盲区判定方法等。判定方法的选择直接影响三轴光电跟踪系统的跟踪精度。理论分析和仿真表明：以Z轴的最大保精角速度来判定地平式盲区的出入点使三轴系统在天顶附近有更高的跟踪精度,且可实现跟踪方式的平稳过渡。