Integrating independent component analysis and local outlier factor for plant-wide process monitoring

We propose a novel process monitoring method integrating independent component analysis (ICA) and local outlier factor (LOF). LOF is a recently developed outlier detection technique which is a density-based outlierness calculation method. In the proposed monitoring scheme, ICA transformation is performed and the control limit of LOF value is obtained based on the normal operating condition (NOC) dataset. Then, at the monitoring phase, the LOF value of current observation is computed at each monitoring time, which determines whether the current process is a fault or not. The comparison experiments are conducted with existing ICA-based monitoring schemes on widely used benchmark processes, a simple multivariate process and the Tennessee Eastman process. The proposed scheme shows the improved accuracy over existing schemes. By adopting LOF, the monitoring statistic is computed regardless of data distribution. Therefore, the proposed scheme integrating ICA and LOF is more suitable for real industry where the monitoring variables are the mixture of Gaussian and non-Gaussian variables, whereas existing ICA-based schemes assume only non-Gaussian distribution.     

A branch and bound method for isolation of faulty variables through missing variable analysis

Fault detection and diagnosis is a critical approach to ensure safe and efficient operation of manufacturing and chemical processing plants. Although multivariate statistical process monitoring has received considerable attention, investigation into the diagnosis of the source or cause of the detected process fault has been relatively limited. This is partially due to the difficulty in isolating multiple variables, which jointly contribute to the occurrence of fault, through conventional contribution analysis. In this work, a method based on probabilistic principal component analysis is proposed for fault isolation. Furthermore, a branch and bound method is developed to handle the combinatorial nature of problem involving finding the contributing variables, which are most likely to be responsible for the occurrence of fault. The efficiency of the method proposed is shown through benchmark examples, such as Tennessee Eastman process, and randomly generated cases.     

Multivariate statistical monitoring of two-dimensional dynamic batch processes utilizing non-Gaussian information

Dynamics are inherent characteristics of batch processes, and they may exist not only within a particular batch, but also from batch to batch. To model and monitor such two-dimensional (2D) batch dynamics, two-dimensional dynamic principal component analysis (2D-DPCA) has been developed. However, the original 2D-DPCA calculates the monitoring control limits based on the multivariate Gaussian distribution assumption which may be invalid because of the existence of 2D dynamics. Moreover, the multiphase features of many batch processes may lead to more significant non-Gaussianity. In this paper, Gaussian mixture model (GMM) is integrated with 2D-DPCA to address the non-Gaussian issue in 2D dynamic batch process monitoring. Joint probability density functions (pdf) are estimated to summarize the information contained in 2D-DPCA subspaces. Consequently, for online monitoring, control limits can be calculated based on the joint pdf. A two-phase fed-batch fermentation process for penicillin production is used to verify the effectiveness of the proposed method.     

Multivariate reduced rank regression in non-Gaussian contexts, using copulas

A new procedure is proposed that performs reduced rank regression (RRR) in non-Gaussian contexts based on multivariate dispersion models. Reduced-rank multivariate dispersion models (RR-MDM) generalize RRR to a very large class of distributions, which include continuous distributions like the normal, Gamma, inverse Gaussian, and discrete distributions like the Poisson, the binomial and the negative binomial. A multivariate distribution is created with the help of the Gaussian copula and estimation is performed using maximum likelihood. It is shown how this method can be amended to deal with the case of discrete data. A Monte Carlo simulation shows that the new estimator is more efficient than the traditional Gaussian RRR. In the framework of MDM's a procedure analogous to canonical correlations is introduced, which takes into account the distribution of the data. Finally, the method is applied to the number of trades of five US department stores on the New York Stock Exchange during the year 1999 and determine the existence of a common factor which represents sector specific news. This analysis is helpful in microstructure analysis to identify leaders from the point of view of dissemination of sectorial information.     

基于独立分量分析的过程监控方法研究

为了解决多变量系统的各个变量之间往往相互影响,且一般不能严格服从高斯分布的问题,采用ICA方法时正常状态下观测的数据进行分析处理,从中提取出统计独立的独立分量,为简化后续分析,对得到的独立分量进行筛选、划分,并分别计算两类统计量:I2统计量和SPE统计量,确定其控制限,与在线数据进行对比,用于监控系统运行.通过一多变量过程仿真实例,证明了这种方法的可靠性,这为ICA应用于监控多变量系统的运行、检测故障的发生提供了有益的思路.     

多元统计性能监视和故障诊断技术研究进展

综述了多元统计分析方法在线性、非线性、多尺度领域中的理论研究进展.分析和总结了连续生产过程和批量间歇生产过程性能监视和故障诊断的应用情况.最后探讨了这一领域中值得进一步研究的问题和可能的发展方向.􀁱     

Improvement of causal analysis using multivariate statistical process control

Statistical process control (SPC) is a conventional means of monitoring software processes and detecting related problems, where the causes of detected problems can be identified using causal analysis. Determining the actual causes of reported problems requires significant effort due to the large number of possible causes. This study presents an approach to detect problems and identify the causes of problems using multivariate SPC. This proposed method can be applied to monitor multiple measures of software process simultaneously. The measures which are detected as the major impacts to the out-of-control signals can be used to identify the causes where the partial least squares (PLS) and statistical hypothesis testing are utilized to validate the identified causes of problems in this study. The main advantage of the proposed approach is that the correlated indices can be monitored simultaneously to facilitate the causal analysis of a software process.

Electrogastrogram extraction using independent component analysis with references

Electrogastrogram (EGG) is a noninvasive measurement of gastric myoelectrical activity cutaneously, which is usually covered by strong artifacts. In this paper, the independent component analysis (ICA) with references was applied to separate the gastric signal from noises. The nonlinear uncorrelatedness between the desired component and references was introduced as a constraint. The results show that the proposed method can extract the desired component corresponding to gastric slow waves directly, avoiding the ordering indeterminacy in ICA. Furthermore, the perturbations in EGG can be suppressed effectively. In summary, it can be a useful method for EGG analysis in research and clinical practice.     

基于数据和知识的工业过程监视及故障诊断综述

从复杂工业过程所可能具有的过程特性及数据存取过程中引入的数据特性分析出发,综述了具有复杂数据特性的工业过程的多元统计监视方法,并分别讨论了基于数据和基于知识方法进行故障诊断的优势、进展、适用范围及二者结合的可能.最后探讨了这一领域中值得进一步研究的问题和可能的发展方向.     

基于PCA的重油分馏塔故障监测与诊断分析

对系统过程数据进行主元分析,建立主元模型,可以在保留原有数据信息特征的基础上消除变量关联和部分系统噪声干扰,从而简化系统分析的复杂度。建立正确的主元模型,结合多变量统计过程控制图(Q统计图,HotellingT2图,主元得分图,贡献图),是对过程对象的进行检测和诊断的一项发展中的技术。通过对一个典型的重油分馏塔运行过程的故障监测与诊断分析,进一步说明了主元模型在确定故障特征方向和多变量统计控制图在监测和诊断故障源上的作用和有效性。同时采用了平均贡献图来直观明确地判别引起系统故障的主要原因。     

基于独立源分析的过程监测方法

多元统计过程控制要求观测数据服从正态分布,而实际的5-业过程数据大都不满足正态分布条件.独立源分析（ICA）近几年才发展起来的一种新的统计方法,可以克服对数据分布的依赖性．对此,以ICA算法为核心,引入一种新型的过程监测方法,应用ICA提取独立源,利用I^2图,Ic^2图和SPE图进行故障检测．最后以3水箱系统为例进行了实验研究,取得了很好的效果．     

基于火焰动态纹理的电熔镁炉工况识别

电熔镁炉制备电熔镁砂的工艺过程中,会交替出现正常熔炼、加料和欠烧等不同工况,针对不同的工况需要采取相应的处理方式来保证生产过程的正常进行。目前,工况的识别主要依靠人工完成,这种方式存在工人劳动强度大、容易漏检误检等问题。本文依据不同工况下炉口火焰具有不同的动态可视化特征,提出一种基于动态纹理的工况识别技术。首先,通过建立炉口火焰的线性动态系统模型来刻画图像的动态特性,然后,设计了基于子空间角度的核函数来度量火焰动态模型相似度。对比实验表明本文设计的基于子空间角度的工况分类器具有更好的分类精度及鲁棒性。     

电阻抗扫描成像计算机辅助诊断方法研究

为了提高电阻抗扫描成像EISI(Electrical Impedance Scanning Imaging)在乳腺疾病检查方面的准确性,将独立分量方法(ICA)应用于EISI数据的特征提取,并结合支持向量机(SVM)进行乳腺疾病计算机辅助诊断(CAD)。实验结果表明:ICA方法所提取的特征对于乳腺疾病分类是有效的。对于良性肿瘤和恶性肿瘤的鉴别,提出的CAD方法灵敏度为74.2%,特异度为82.8%,准确度为80%。结合所提出的CAD方法和传统的EISI检查方法,可以提高EISI在乳腺疾病检查方面的可靠性,降低假阳性率,避免对良性肿瘤患者进行活检。     

基于多级动态主元分析的电熔镁炉异常工况诊断

电熔镁熔炼过程中的异常工况(如半熔化工况)直接影响产品质量、威胁人员和生产安全, 有必要及时诊断. 但与异常直接相关的超高温熔池温度(>2850 ℃)难以利用温度传感器检测, 目前现场主要依靠工人在定期巡检时人眼观察炉壁来诊断, 工作强度大、安全度低、诊断不及时. 针对上述问题, 本文提出一种炉体动态图像驱动的电熔镁炉异常工况实时诊断方法. 结合电熔镁炉熔炼各区域温度分布的空间特征、正常工况下熔炼温度变化和水雾扰动引入的图像时序特征、以及异常工况下温度异常区域持续发亮扩大的特征, 在对炉体动态图像进行空间多级划分的基础上, 提出了一种多级动态主元分析(Multi-level dynamic principal component analysis, MLDPCA) 动态图像分块建模方法. 在此基础上, 提出基于MLDPCA的逐级诊断方法与基于贡献图的异常定位方法. 最后, 采用某电熔镁生产现场的实际图像进行方法验证, 结果表明了所提方法的有效性.     

间歇过程多变量统计过程控制的理论与方法

统计过程控制技术作为一种用统计分析方法保证产品质量和生产稳定性的手段,在现代工业生产中的应用日益广泛。阃歇生产过程因其过程变量的时间相关性和变量之间大多存在强非线性关系的特点,采用传统的统计过程控制方法难以满足其对产品高质量的要求。通过多元投影的方法压缩过程变量的维数,在较低维的主元空间对过程进行监控。可以较好的解决上述矛盾。针对间歇过程运行的特点,分析了线性和非线性多元统计过程控制技术的理论和方法。     

基于缺失数据的误差生成策略及其在故障检测中的应用

误差生成是基于机理模型故障检测方法的核心本质,但鲜有应用于统计过程监测方法中.为此,提出一种基于缺失数据的误差生成策略,将能反映出采样数据对统计模型拟合程度的误差作为新的被监测对象实施故障检测.所提出的基于缺失数据的主元分析(MD-PCA)方法通过逐一假设各变量测量数据缺失后,利用缺失数据处理方法推测出相应缺失数据的估计值,并对缺失数据的实际值与估计值之间的误差实施基于PCA模型的故障检测.利用误差实施故障检测的优势在于,生成的误差能在一定程度上降低原测量变量的非高斯性程度,而且误差体现的是对应缺失变量中与其他测量变量不相关的成分信息,更能揭示各测量变量的本质.通过在TE过程上的实验充分验证了所提出方法的优势,以及MD-PCA方法用于故障检测的可行性与优越性.