Fault identification for process monitoring using kernel principal component analysis

In this research, we develop a new fault identification method for kernel principal component analysis (kernel PCA). Although it has been proved that kernel PCA is superior to linear PCA for fault detection, the fault identification method theoretically derived from the kernel PCA has not been found anywhere. Using the gradient of kernel function, we define two new statistics which represent the contribution of each variable to the monitoring statistics, Hotelling's T²and squared prediction error (SPE) of kernel PCA, respectively. The proposed statistics which have similar concept to contributions in linear PCA are directly derived from the mathematical formulation of kernel PCA and thus they are straightforward to understand. The main contribution of this work is that we firstly suggest a fault identification method especially applicable to process monitoring using kernel PCA. To demonstrate the performance, the proposed method is applied to two simulated processes, one is a simple nonlinear process and the other is a non-isothermal CSTR process. The simulation results show that the proposed method effectively identifies the source of various types of faults.     

蒸汽计量系统的改造及实施方案

通过对一个蒸汽计量系统的实施方案的简单介绍 ,为老企业蒸汽计量系统的改造提供了一种新的经济可行的思路。同时简单介绍了软件开发相关的两个关键控件 :Timer控件和MSComm控件。     

气固相氢氟化催化剂的研究

对于气固相氢氟化反应制备七氟丙烷的催化剂,进行了系统的研究。采用浸渍法制备催化剂,当催化活性组分Ａ为载体的８．５％～１０％,组分Ｃ２为１．０％～２．５％、二者之和为１１％时,达到了较佳的催化性能,将其应用于由六氟丙烯制备七氟丙烷时,在反应温度≥２１０℃时,粗品气中的Ｆ－２２７含量≥９８．５％。     

Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Identifying the presence of anti-nuclear antibody (ANA) in human epithelial type 2 (HEp-2) cells via the indirect immunofluorescence (IIF) protocol is commonly used to diagnose various connective tissue diseases in clinical pathology tests. As it is a labour and time intensive diagnostic process, several computer aided diagnostic (CAD) systems have been proposed. However, the existing CAD systems suffer from numerous shortcomings due to the selection of features, which is commonly based on expert experience. Such a choice of features may not work well when the CAD systems are retasked to another dataset. To address this, in our previous work, we proposed a novel approach that learns a set of filters from HEp-2 cell images. It is inspired by the receptive fields in the mammalian's vision system, since the receptive fields can be thought as a set of filters for similar shapes. We obtain robust filters for HEp-2 cell classification by employing the independent component analysis (ICA) framework. Although, this approach may be held back due to one particular problem; ICA learning requires a sufficiently large volume of training data which is not always available. In this paper, we demonstrate a biologically inspired solution to address this issue via the use of spontaneous activity patterns (SAP). The spontaneous activity patterns, which are related to the spontaneous neural activities initialised by the chemical release in the brain, are found as the typical stimuli for the visual cell development of newborn animals. In the classification system for HEp-2 cells, we propose to model SAP as a set of small image patches containing randomly positioned Gaussian spots. The SAP image patches are generated and mixed with the training images in order to learn filters via the ICA framework. The obtained filters are adopted to extract the set of responses from a HEp-2 cell image. We then employ regions from this set of responses and stack them into “cubic regions”, and apply a classification based on the correlation information of the features. We show that applying the additional SAP leads to a better classification performance on HEp-2 cell images compared to using only the existing patterns for training ICA filters. The improvement on classification is particularly significant when there are not enough specimen images available in the training set, as SAP adds more variations to the existing data that makes the learned ICA model more robust. We show that the proposed approach consistently outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.     

热重法研究环氧树脂胶粘剂

用热重法研究环氧树脂胶粘剂，研究结果表明热重法能较好地研究环氧树脂胶粘剂的组成与性能，研究结果还表明固化剂中过量的游离胺与惰性稀释剂的存在不利于固化胶粘剂的力学性能，有利于其热稳定性。     

基于KPCA-PSO-SVM的径流预测研究

为提高径流预测模型的准确性与稳定性,对KPCA-PSO-SVM的径流预测方法进行了研究。在分析径流影响因素的基础上,利用核主成分分析(KPCA)法对径流影响因子进行非线性特征提取,获得主成分作为支持向量机(SVM)的输入变量,建立了径流预测SVM模型,其中模型参数通过粒子群算法(PSO)进行优化。模型建立后,以新疆伊犁河雅马渡站中长期径流预测为例进行分析。预测分析结果表明,在拟合和检验阶段模型的平均相对误差分别为0.77%和7.64%,与其他预测模型比较,基于KPCA-PSO-SVM方法建立的径流预测模型有较好的预测和泛化能力,是一种行之有效的中长期径流预测方法。     

一种改进的动态核主元分析故障检测方法

针对复杂工业系统动态非线性故障检测过程精度低和计算量大的问题,提出了一种改进的动态核主元分析故障检测方法，该方法首先利用不可区分度剔除相关程度较小或者不相关变量,减少数据量，然后通过观测值扩展对筛选后的新数据构建增广矩阵，并对矩阵使用核主元分析提取变量数据的非线性空间相关特征，最后通过监测T ²和SPE 两种统计量诊断出系统发生故障及识别故障变量。仿真实验证明，该方法能对风力发电机故障进行有效监测和诊断，与KPCA方法相比，改进的动态核主元分析方法对微小故障更为敏感。     

面向能力组件的项目管理系统研究

当前企业面临的多项目管理环境日益突出,如何结合管理创新,搭建有效的项目管理平台,支撑多项目目标实现是众多企业面临的突出问题。文章结合装备研制,分析了多项目环境下传统管理方式存在的不足,提出了能力组件和计划视图的概念,构建了一种基于能力组件的项目管理方法和分级计划管理模型,设计并开发了面向能力组件的项目管理系统。详细说明了系统的技术架构、功能组成和关键技术,并结合应用实践,介绍了一种基于能力组件的项目管理组织模式和计划制定过程。     

低温烘烤型双组分水性聚氨酯涂料

采用溶液聚合法，合成了丙烯酸五元共聚物；将该共聚物中的羧基中和后制成丙烯酸多元醇分散体，将其作为B组分与一种低黏度的多异氰酸酯固化剂(A组分)组成双组分水性聚氨酯涂料。研究发现：引发剂含量、羟基含量、中和度以及A、B两组分混合搅拌方式都对双组分涂膜的性能产生重要影响。当引发剂含量为1％，羟基含量为2．7％，中和度为100％，A、B组分采用机械搅拌混合时所得的涂膜综合性能最好。水性涂料的适用期达到8h，超过相应的溶剂型涂料。     

面向图像局部重建的梯度分析

在基于场方向导数的图像局部区域重建过程中,梯度计算对图像边缘的重建效果有很大影响。分析选择单向计算梯度分量的必要性,提出梯度计算的就近原则,对参与梯度分量单向计算的相邻点设置优先级,将缺损点与已知点之间的边缘特征反映在已知点的梯度中,进而影响重建过程,从而实现对图像中边缘特征的重建。实验结果表明,采用该梯度计算模式能有效改善对图像边缘特征的重建效果。