A Kernel Time Structure Independent Component Analysis Method for Nonlinear Process Monitoring

Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis(KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC feature.Finally, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA.

Efficient Fast Independent Component Analysis Algorithm with Fifth-Order Convergence

Independent component analysis （ICA） is the primary statistical method for solving the problems of blind source separation. The fast ICA is a famous and excellent algorithm and its contrast function is optimized by the quadratic convergence of Newton iteration method. In order to improve the convergence speed and the separation precision of the fast ICA, an improved fast ICA algorithm is presented. The algorithm introduces an efficient Newton＇s iterative method with fifth-order convergence for optimizing the contrast function and gives the detail derivation process and the corresponding condition. The experimental results demonstrate that the convergence speed and the separation precision of the improved algorithm are better than that of the fast ICA.

Multi-state Information Dimension Reduction Based on Particle Swarm Optimization-Kernel Independent Component Analysis

The precision of the kernel independent component analysis( KICA) algorithm depends on the type and parameter values of kernel function. Therefore,it's of great significance to study the choice method of KICA's kernel parameters for improving its feature dimension reduction result. In this paper, a fitness function was established by use of the ideal of Fisher discrimination function firstly. Then the global optimal solution of fitness function was searched by particle swarm optimization( PSO) algorithm and a multi-state information dimension reduction algorithm based on PSO-KICA was established. Finally,the validity of this algorithm to enhance the precision of feature dimension reduction has been proven.

Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis

A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis （KICA）. The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently devel- oped statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of mea- surements and it is a two-phase algorithm., whitened kernel principal component analysis （KPCA） plus indepen- dent component analysis （ICA）. The application to the fluid catalytic cracking unit （FCCU） simulated process in- dicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear rela- tionship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for lonu-term performance deterioration.

Small Target Extraction Based on Independent Component Analysis for Hyperspectral Imagery

A small target detection approach based on independent component analysis for hyperspectral data is put forward. In this algorithm, firstly the fast independent component analysis(FICA) is used to collect target information hided in high-dimensional data and projects them into low-dimensional space.Secondly, the feature images are selected with kurtosis .At last, small targets are extracted with histogram image segmentation which has been labeled by skewness.

CEEMD-FastICA-CWT联合瞬态响应阶次的电驱总成噪声源识别

以某增程式电驱动总成为研究对象,提出基于联合算法的噪声分离识别模型。首先,采用互补集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)联合快速独立分量分析(fast independent component analysis,FastICA)方法提取纯电模式稳态工况下单一通道噪声信号特征,利用复Morlet小波变换及FFT对各分量信号时频特性进行识别。其次,采用阶次分析法和声能叠加法对稳态分量信号对应的各瞬态响应阶次能量进行对比分析,并结合皮尔逊积矩相关系数(Pearson product moment correlation coefficient,PPMCC)相似性识别确定不同噪声激励源贡献度。结果表明:减速齿副啮合噪声对该增程式电驱总成纯电模式运行噪声整体贡献度最大。

基于FastICA-LDA的光伏并网逆变器故障诊断

为了实现逆变器开路故障诊断,提出了一种新的诊断方法。该方法采用快速独立成分分析算法判定逆变器是否发生单管开路故障,如果发生单管开路故障,计算旋转电流Id频域下的特征值,将这些特征值作为线性判别分析模型的输入值,最后由LDA模型输出逆变器工作状态编号,从而实现单管开路定位。经过MATLAB仿真验证表明,所提方法对光伏并网逆变器故障的诊断效果较好。

一种融合KPCA、FastICA及SVD的腹壁源胎儿心电 信号提取算法研究

目的:为实现从母体腹壁混合信号中提取高信噪比和波形清晰的胎儿心电信号,提出一种融合核主成分分析(kernel principal component analysis,KPCA)、快速独立成分分析(fast independent component analysis,FastICA)及奇异值分解(singular value decomposition,SVD)的胎儿心电信号提取算法。方法:首先,采用KPCA对母体心电信号进行降维,再利用改进的基于负熵的FastICA处理降维后的数据,得到独立成分。随后,引入样本熵进行信号通道选择,挑选出包含最多母体信息的信号通道。在选中的母体通道上进行SVD,得到母体心电信号的近似估计,再用腹壁源信号减去该信号得到胎儿心电的初步估计。最后,采用改进的基于负熵的FastICA成功分离出纯净的胎儿心电信号。在腹部和直接胎儿心电图数据库(Abdominal and Direct Fetal Electrocardiogram Database,ADFECGDB)和PhysioNet 2013挑战赛数据库中对提出的算法进行验证。结果:提出的算法在主观视觉效果和客观评价指标上都表现出优越的性能。在ADFECGDB数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.74%、98.85%和99.30%;在PhysioNet 2013挑战赛数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.10%、97.87%和98.48%。结论:融合KPCA、FastICA及SVD的胎儿心电信号提取算法在提取胎儿心电信号的同时有效处理了附加噪声,为胎儿疾病的早期诊断提供了有力支持。

An Independent Component Analysis Algorithm through Solving Gradient Equation Combined with Kernel Density Estimation

A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introduced to solve this equation efficiently.The unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density method.Computer simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm.

FSS-kernel与FastICA融合的盲源分离算法研究

Fast ICA算法有着比传统ICA算法更快、更稳健的收敛速度,但由于其选用的非线性函数不能很好地符合源信号的统计特性,恢复结果并不理想。针对该问题,提出了一种有限支持样本核函数(FSS-kernel)与Fast ICA融合的盲源分离算法。该方法是通过FSS-kernel算法估计得出源信号概率密度函数,结合Fast ICA算法,实现混合信号的盲分离。仿真结果表明,该方法能够有效地完成混叠信号的分离,通过与传统ICA算法及Fast ICA算法比较,证明了该方法具有更高的分离精度和自适应能力。

基于FastICA和G-G聚类的多元时序自适应分段

现有多元时间序列的分段方法主要通过检测时序数据统计特性或形状的变化情况,并以此为依据对分段点的位置进行“硬划分”.然而,这些分段方法无法对两个分段之间的过渡区间长度进行准确估计,且普遍需要人为预先设置参数,在高维且噪声较强的情况下分段效果较差.本文针对现有分段方法存在的诸多不足,提出一种基于FastICA(Fast Independent Component Analysis)和G-G(Gath-Geva)模糊聚类的多元时序自适应分段方法 .该方法利用FastICA进行特征提取,采用DW(Durbin-Watson)指数自动选取高信噪比的主成分,并根据最小描述长度(Minimum Description Length,MDL)设计基于G-G模糊聚类的自适应分段模型,实现对于多元时间序列的“软划分”.基于多种领域的真实数据集实验结果表明:与现有主流的分段方法相比,本文方法在上述数据集上的平均F1和MAE(Mean Absolute Error)可分别提升8.4%~16.8%和3.06%~6.56%.

基于FEWT-FastICA的滚动轴承故障特征识别方法

滚动轴承故障信号常包含着大量的噪声,并以调制的形式存在,其故障特征信息提取困难;同时,采用快速经验小波变换(FEWT)分解故障信号时,又存在故障特征被削弱的问题。为此,将FEWT与快速独立分量分析(FastICA)的优点相结合,在此基础上提出了一种基于FEWT-FastICA的滚动轴承故障特征识别方法。首先,利用FEWT算法对轴承故障信号进行了分解,得到了一组固有模态分量(IMF);根据峭度准则,将峭度值大于3的IMF分量重构为振动冲击信号,峭度值小于3的IMF分量重构为虚拟通道信号;然后,将重构后的信号输入FastICA算法,进行信号的降噪解混,得到信号的最佳估计信号,对最佳估计信号进行了包络谱分析,完成了对滚动轴承的故障诊断;最后,为了验证FEWT-FastICA算法的有效性,采用仿真信号及真实轴承故障信号分别进行了实验验证;同时,为了验证FEWT-FastICA算法的优越性,将其与FEWT进行了对比分析。研究结果表明:该方法能有效地提取故障特征信息,比FEWT方法所得结果的信噪比提升了1.55倍,为轴承故障诊断提供了一种新方法。

基于复数FastICA的双极化干扰对消算法研究

为解决卫星通信中极化复用导致的交叉极化干扰问题,提出了一种基于复数快速独立成分分析方法,根据传输信道的对称性对算法作了简化处理,减少了计算量。该算法依据发送端的两个线极化信号的不相关性,在无源信号及混合矩阵的先验信息情况下,构造负熵函数并使其最大化来分离出独立成分,进而实现交叉极化信号对消。仿真实验以正交相移键控调制信号为例,从误差向量幅度、信干噪比、交叉极化隔离度、性能指数这几个指标上进行仿真,仿真结果显示基于复数快速独立成分分析方法对解决交叉极化干扰问题具有良好的性能。

Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being challenged.To address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone safety.We deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone data.By default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of themodel.To improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV data.Based on the above improvements,we create a novel anomaly detection strategy FastICA-TGAK-OCELM.The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)dataset.The experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.

基于实测数据的牵引供电系统并联谐波谐振概率识别方法

当前,我国高速铁路电力机车广泛采用脉冲宽度调制的四象限变流器,在其性能改善的同时也带来了频带更宽的高次谐波问题,更易激发牵引供电系统谐波谐振。谐波谐振可能会引起过电压/过电流,严重时引发设备爆炸、保护误动等安全事故。因此,准确识别系统潜在的谐振频率具有重要意义。目前常用的频率扫描分析、谐振模态分析等方法依赖于系统拓扑和模型的精确性,而基于实测数据的传统识别方法的准确性受系统参数波动和背景谐波影响较大。为此,提出了一种基于实测数据的牵引供电系统并联谐振概率识别方法,以电压、电流波形的余弦相似度作为谐振预判条件以减少无效计算,并综合基波/谐波功率因数、系统谐波阻抗幅值等多层次谐振特征进行概率分析,克服了系统参数波动和背景谐波影响。仿真研究、实测数据分析与已有研究方法对比,表明所提方法在负荷波动性强、背景谐波较大时仍然适用,且识别的准确性更高,验证了该方法的有效性和可靠性。

基于FKICA-SIFT特征的合成孔径图像多尺度配准

针对合成孔径(SAR)图像的配准,提出一种基于仿射不变快速核独立成分分析-尺度不变特征变换(FKICA-SIFT)的多尺度配准方法。首先,根据特征点的Hessian矩阵构建仿射不变SIFT描述子。接着,利用FKICA提取该描述子的独立成分得到新的描述子FKICA-SIFT。然后,利用该描述子对Steerable滤波后的各层带通合成子图像提取的特征点进行匹配。最后,采用由粗到细的匹配策略逐步优化变换参数,实现图像的多尺度精确配准。实验结果表明,对有较大仿射变化的SAR图像,当阈值小于0.7时,该方法的匹配正确率大于85%,阈值小于0.5时,匹配正确率可达90%以上,配准精度达到亚像素水平,优于SIFT,PCA-SIFT,ICA-SIFT及SURF等相关方法。使用该方法准确地检测出了地震前后唐家山堰塞湖水域的变化情况,基本满足了SAR图像变换检测前精确配准的要求。

基于改进Fast-ICA的电能质量谐波检测

独立分量分析是利用信号的高阶统计量快速准确地实现信号分离和恢复。提出一种改进快速分离算法检测电能质量谐波的方法。在介绍了快速分离算法基本原理的基础上对牛顿迭代法进行了改进,减少了迭代次数,提高了收敛速度;依据负熵极大的独立性准则实现谐波信号的盲分离,再进行幅值修正以实现对真实信号的估计;对分离修正后的信号进行FFT分析,得到幅值和频率,进而实现对谐波的检测。仿真实验结果表明了该算法在检测精度和运行效率上都有所提高。

基于改进FastICA算法的入侵检测样本数据优化方法

为更好实现对入侵检测样本数据的优化处理,提出了一种改进的快速独立成分分析(Fast ICA)算法,采用基于加权相关系数进行白化处理以减少信息损失,并优化牛顿迭代法使其满足三阶收敛。对算法进行了细致描述,分析了算法的时间复杂度。实验结果表明,该方法可有效减少数据信息损失,具有迭代次数少、收敛速度快等优点,可有效提高入侵检测样本数据的优化效率。

Fast ICA盲分离算法在雷达抗主瓣干扰中的应用研究

压制干扰信号从主瓣进入雷达天线,会严重影响雷达的性能,通常的副瓣抗干扰技术难以奏效。文中首先给出了Fast ICA应用于雷达抗主瓣干扰的信号模型;在高信噪比的均匀噪声环境中,利用基于寻找峭度的局部极值点的Fast ICA盲分离算法分离接收到的主瓣干扰混合信号,通过脉压找出目标信号。仿真验证了算法用于抗主瓣干扰的有效性,该算法具有良好的抗干扰性能,在分离效率上具有较明显的优势。

基于FastICA-MP算法的次同步振荡模态参数辨识

WAMS在电力系统中的应用越来越广,使得电力系统次同步振荡模态参数在线辨识成为可能。但系统中存在大量电力电子设备,造成了WAMS采样信号中存在较强的噪声干扰,影响了振荡模态参数辨识的准确性。鉴于快速独立分量分析可以实现噪声信号与原始信号的有效分离,提出首先通过快速独立分量分析对采样信号进行预处理,然后将滤噪后的信号通过矩阵束算法进行辨识得到振荡模态参数。通过此方法可以进一步提高矩阵束的辨识准确度。通过理想仿真算例和国内某特高压直流输电系统作为实际仿真算例进行分析。仿真结果表明,快速独立分量分析可有效分离噪声信号,提高了矩阵束辨识准确性,为后续阻尼控制器的设计奠定了基础。