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。

Source Separation of Diesel Engine Vibration Based on the Empirical Mode Decomposition and Independent Component Analysis

Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements,such as independent component analysis（ICA）.However,the source separation of vibration signal from single-channel is impossible.In order to study the source separation from single-channel signal for the purpose of source extraction,the combination method of empirical mode decomposition（EMD） and ICA is proposed in diesel engine signal processing.The performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared,and the results show that EMD-ICA method outperforms the other,and overcomes the drawback of ICA in the case of single-channel measurement.The independent source signal components can be separated and identified effectively from one-channel measurement by EMD-ICA.Hence,EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements.

Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation

The estimation of oil spill coverage is an important part of monitoring of oil spills at sea.The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills and the accuracy of estimates of their size.We consider at-sea oil spills with zonal distribution in this paper and improve the traditional independent component analysis algorithm.For each independent component we added two constraint conditions:non-negativity and constant sum.We use priority weighting by higher-order statistics,and then the spectral angle match method to overcome the order nondeterminacy.By these steps,endmembers can be extracted and abundance quantified simultaneously.To examine the coverage of a real oil spill and correct our estimate,a simulation experiment and a real experiment were designed using the algorithm described above.The result indicated that,for the simulation data,the abundance estimation error is 2.52% and minimum root mean square error of the reconstructed image is 0.030 6.We estimated the oil spill rate and area based on eight hyper-spectral remote sensing images collected by an airborne survey of Shandong Changdao in 2011.The total oil spill area was 0.224 km^2,and the oil spill rate was 22.89%.The method we demonstrate in this paper can be used for the automatic monitoring of oil spill coverage rates.It also allows the accurate estimation of the oil spill area.

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

In this paper, a new method to reduce noises within chaotic signals based on ICA （independent component analysis） and EMD （empirical mode decomposition） is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the indepen- dent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions （IMFs） of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor.

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。

Evaluation of Dissolved Organic Carbon Using Synchronized Fluorescence Emission Spectra and Unsupervised Method of Principal Component Analysis (PCA) and Independent Component Analysis (ICA)

Dissolved organic matter (DOM) can be originated from autochthonous or allochthonous sources, where allochthonous DOM can be from pedogenic sources (humic substances—HSs) or anthropogenicsources (wastewater). The analysis of fluorescence emission, excitation, synchronous or excitation-emission matrix (EEM) have been used to identify the main source or probable contribution of dissolved compounds, such as humic acids (HA), fulvic acids (FA) and dissolved organic carbon (DOC) from sewage, but does not quantify. Fluorescence emission is a powerful technique to detect and qualify organic dissolved compounds but fails in quantitative aspects. In this work, we propose an in situ method for direct determination of DOC using synchronous fluorescence spectra with independent component analysis (ICA). Well known standard solutions were used for method development and validation. In this work, we show that it is possible to predict the number of independent contributions using an unsupervised method based on iterative Principal Component Analysis and Independent Component Analysis (PCA-ICA) approach over combined matrix results. Within these results it’s also possible to see that with a very small amount of independent components it is possible to describe environmental samples of HA, FA and primary productivity (PP).

基于ICEEMD-FastICA的滚动轴承故障诊断方法

针对滚动轴承早期故障特征信号提取困难的问题,提出了一种改进完备集成经验模态分解(Improved Complete Ensemble Empirical Mode Decomposition,ICEEMD)和独立分量分析(Independent Component Analysis,ICA)联合故障诊断方法。该方法利用峭度准则将经ICEEMD得到的固有模态分量(Intrinsic Mode Function,IMF)重构后结合快速独立分量分析(Fast Independent Component Analysis,FastICA)进行降噪解混,明显降低被测信号中的噪声,并且在故障特征频率处能量幅值取得最大值,便于辨识故障特征。通过试验研究分析,表明该方法可以明显降低噪声干扰,突出故障频率成分。和ICEEMD与包络谱结合的方法对比,信噪比提高了29.54%,能更准确地识别故障特征,达到对滚动轴承故障的判别需求,从而为轴承故障特征提取提供了一种新思路。

Neuropathological characteristics of abnormal white matter functional signaling in adolescents with major depression

BACKGROUND Major depression disorder(MDD)constitutes a significant mental health concern.Epidemiological surveys indicate that the lifetime prevalence of depression in adolescents is much higher than that in adults,with a corresponding increased risk of suicide.In studying brain dysfunction associated with MDD in adolescents,research on brain white matter(WM)is sparse.Some researchers even mistakenly regard the signals generated by the WM as noise points.In fact,studies have shown that WM exhibits similar blood oxygen level-dependent signal fluctuations.The alterations in WM signals and their relationship with disease severity in adolescents with MDD remain unclear.AIM To explore potential abnormalities in WM functional signals in adolescents with MDD.METHODS This study involved 48 adolescent patients with MDD and 31 healthy controls(HC).All participants were assessed using the Patient Health Questionnaire-9 Scale and the mini international neuropsychiatric interview(MINI)suicide inventory.In addition,a Siemens Skyra 3.0T magnetic resonance scanner was used to obtain the subjects'image data.The DPABI software was utilized to calculate the WM signal of the fractional amplitude of low frequency fluctuations(fALFF)and regional homogeneity,followed by a two-sample t-test between the MDD and HC groups.Independent component analysis(ICA)was also used to evaluate the WM functional signal.Pearson’s correlation was performed to assess the relationship between statistical test results and clinical scales.RESULTS Compared to HC,individuals with MDD demonstrated a decrease in the fALFF of WM in the corpus callosum body,left posterior limb of the internal capsule,right superior corona radiata,and bilateral posterior corona radiata[P<0.001,family-wise error(FWE)voxel correction].The regional homogeneity of WM increased in the right posterior limb of internal capsule and left superior corona radiata,and decreased in the left superior longitudinal fasciculus(P<0.001,FWE voxel correction).The ICA results of WM overlapped with those of regional homogeneity.The fALFF of WM signal in the left posterior limb of the internal capsule was negatively correlated with the MINI suicide scale(P=0.026,r=-0.32),and the right posterior corona radiata was also negatively correlated with the MINI suicide scale(P=0.047,r=-0.288).CONCLUSION Adolescents with MDD involves changes in WM functional signals,and these differences in brain regions may increase the risk of suicide.

基于SOA-VMD-ICA的海水泵激励源特征提取方法

针对海水泵复杂多源激励特征提取问题,提出了一种海鸥优化算法(SOA)、变分模态分解(VMD)和独立分量分析(ICA)相结合的海水泵激励源特征提取方法。基于单通道测量信号,采用VMD算法与SOA算法选取信号平方包络谱峭度统计量作为适应度函数,寻优获取模态分解数量K、惩罚系数α及特征模态函数(IMF)分量。采用信号排列熵作为噪声检验函数,合理选取排列熵阈值,对IMF分量进行噪声筛选,获取非噪声IMF分量信号。将非噪声IMF分量与原输入信号组合,采用快速独立成分分析(Fast-ICA)算法计算得到激励源信号向量,从而实现激励源特征信号的提取。通过实船海水泵激励源特征提取试验及对比分析,验证了所提方法的有效性。研究结果表明,所提的SOA-VMD-ICA方法能满足单通道测量条件海水泵激励源特征提取准确性要求。

Identification of essential language areas by combination of fMRI from different tasks using probabilistic independent component analysis

Functional magnetic resonance imaging (fMRI) has been used to lateralize and localize lan-guage areas for pre-operative planning pur-poses. To identify the essential language areas from this kind of observation method, we pro-pose an analysis strategy to combine fMRI data from two different tasks using probabilistic in-dependent component analysis (PICA). The assumption is that the independent compo-nents separated by PICA identify the networks activated by both tasks. The results from a study of twelve normal subjects showed that a language-specific component was consistently identified, with the participating networks sepa-rated into different components. Compared with a model-based method, PICA’s ability to capture the neural networks whose temporal activity may deviate from the task timing suggests that PICA may be more appropriate for analyzing language fMRI data with complex event-related paradigms, and may be particularly helpful for patient studies. This proposed strategy has the potential to improve the correlation between fMRI and invasive techniques which can dem-onstrate essential areas and which remain the clinical gold standard.

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.

Two Dimensional Spatial Independent Component Analysis and Its Application in fMRI Data Process

One important application of independent component analysis （ICA） is in image processing. A two dimensional （2-D） composite ICA algorithm framework for 2-D image independent component analysis （2-D ICA） is proposed. The 2-D nature of the algorithm provides it an advantage of circumventing the roundabout transforming procedures between two dimensional （2-D） image deta and one-dimensional （l-D） signal. Moreover the combination of the Newton （fixed-point algorithm） and natural gradient algorithms in this composite algorithm increases its efficiency and robustness. The convincing results of a successful example in functional magnetic resonance imaging （fMRI） show the potential application of composite 2-D ICA in the brain activity detection.

基于CPSO-ICA的航空瞬变电磁信号去噪方法研究

为解决航空瞬变电磁检测信号易受到外部噪声干扰进而严重影响测量数据质量与可靠性的问题,提出使用独立成分分析(ICA)法,对检测信号进行处理,通过分离原始信号中的有效信号和噪声信号,实现信号去噪。为解决步长设定问题,将混沌粒子群优化算法(CPSO)引入到ICA中(CPSO-ICA),使用粒子群算法实时动态调整ICA的步长函数,进一步使用混沌算法优化标准粒子群算法以实现步长的全局寻优,加快收敛速度的同时减小稳态误差,增强ICA算法的去噪效果。模拟信号与实测数据的实验结果表明,CPSO-ICA算法能够在保证去噪效果的同时,完整地保留原始信号的特征,能够为后期反演提供可靠和有效的航空瞬变电磁数据.

基于TICA的太阳能电池无监督学习缺陷识别

针对传统图像特征提取方法用于缺陷检测存在自适应程度低的问题,提出了一种基于拓扑独立成分分析(TICA)的无监督图像特征提取缺陷识别方法。首先通过TICA算法从缺陷集中自适应地估计基向量,利用基向量对应滤波器与缺陷图像进行滤波,提取滤波响应作为特征,为避免TICA算法陷入局部最优,引入差分进化(DE)算法进行优化。然后采用ReliefF算法和K-means算法对提取特征进行选择,减少特征中冗余和无关信息,降低特征向量维数。最后利用随机森林分类器对样本进行缺陷分类,目前总体识别准确率高达96.0%,验证了所提出方法的有效性。

SIGNAL FEATURE EXTRACTION BASED UPON INDEPENDENT COMPONENT ANALYSIS AND WAVELET TRANSFORM

It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent component analysis (ICA) method is combined withwavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function offrequency band chosen with multi-resolution wavelet transform can be used to judge whether thestochastic disturbance singular signal is interfused. By these ways, the vibration signals can beextracted effectively, which provides favorable condition for subsequent feature detection ofvibration signal and fault diagnosis.

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MELMD-ICA的光纤振动信号降噪方法

针对分布式光纤传感系统所采集含噪信号,提出一种改进集成局部均值分解(MELMD)联合独立成分分析(ICA)的降噪方法,引入排列熵判决机制提高抑制模态混叠与虚假分量能力。首先使用MELMD方法分解含噪信号得到乘积函数(PF)并进行信号重构;将含噪信号和重构信号求差得到虚拟噪声,构造虚拟通道;然后使用ICA对含噪信号和虚拟通道进行信噪分离,得到最终结果。通过实验验证,该方法与EMD-ICA,EEMD-ICA,MELMD相比,能更好地消除信号中的噪声,保留信号的特征信息。

Independent component analysis approach for fault diagnosis of condenser system in thermal power plant

A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.

基于RVMD-RobustICA-ST联合相干性分析的电驱动总成噪声源识别

为研究集成一体化电驱动总成的噪声源特性,提出一种RVMD-RobustICA-ST联合算法融合相干性分析的噪声源识别方法。首先,采用基于奇异值分解的占优特征值准则估计噪声子空间维数对变分模态分解参数进行指向性条件约束,并利用鲁棒性独立分量分析联合占优特征值约束的变分模态分解对信号特征进行提取。然后,利用S变换和快速傅里叶变换对各分量信号时频特性进行识别。最后,在二次残差法分析分量信号波形误差度基础上,以电驱动总成振动信号、噪声信号、时频重叠分量信号为变量建立线性系统并进行相干性分析。结果表明,稳态工况下减速二级齿副啮合振动噪声对该电驱动总成噪声贡献度最大,且时频重叠分量信号的噪声能量主要由减速一级齿副啮合振动提供。

Study of engine noise based on independent component analysis

Independent component analysis was applied to analyze the acoustic signals from diesel engine. First the basic prin-ciple of independent component analysis (ICA) was reviewed. Diesel engine acoustic signal was decomposed into several inde-pendent components (ICs); Fourier transform and continuous wavelet transform (CWT) were applied to analyze the independent components. Different noise sources of the diesel engine were separated, based on the characteristics of different component in time-frequency domain.