基于变量分块的KDLV-DWSVDD间歇过程故障检测算法研究

非线性动态间歇过程中,测量变量存在不同的序列相关性,且变量间的交叉相关性会体现在不同的采样时刻上,然而传统检测方法没有考虑这种变量间的相关性,通常将所有变量视为独立或相关关系进行特征提取,不能充分提取到故障信息的特征,造成监测效果不佳.因此,提出一种基于变量分块的核动态潜变量-动态加权支持向量数据描述(KDLV-DWS...     

Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis

In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis.     

改进MKPCA方法及其在发酵过程监控中的应用

针对间歇发酵过程缓慢时变和非线性等特点,提出一种基于滑动窗技术的多向核主元分析(MWMKPCA)方法.该方法结合了核主元分析(KPCA)和滑动窗口技术的优点,其中KPCA能有效解决过程数据的非线性问题,保证数据信息抽取的完整性;而滑动窗口技术能有效避免MKPCA在线应用时预报未来测量值所引入的误差,提高监控性能.对于已判断正常的新批次过程数据,将其加入模型参考数据库进行更新,从而提高间歇过程性能检测的准确性.将该方法应用到工业青霉素发酵过程的监控中,并与MPCA、MKPCA方法的监测性能进行了比较.结果表明:该方法能有效提取过程变量间的非线性关系,降低运行过程的误报率,对缓慢时变的间歇过程具有更可靠的检测性能.     

基于DMOLPP的间歇过程在线故障检测

为了保持过程数据集的局部结构,确保数据投影后的投影向量正交,降低数据误差重构方面的难度,提出了一种基于动态多向正交局部保持投影(DMOLPP)进行间歇过程故障检测的方法。该方法将滑动窗口技术和正交局部保持投影(OLPP)相结合用于间歇过程在线检测。首先,将批次数据展开成二维数据,利用滑动窗口技术分别在时间片内运用OLPP算法提取能表征过程正常数据内在局部近邻结构的特征;然后,对于新来批次数据标准化处理后分别在相应窗口内投影,提取特征向量;最后利用核密度估计(KDE)确定控制限进行过程检测。通过仿真结果表明,运用DMOLPP算法检测到故障发生的时刻早于动态多向局部保持投影(DMLPP)、动态多向邻域保持嵌入(DMNPE)方法。与动态多向主元分析(DMPCA)相比,具有较低或者无误报时刻,验证了该方法的有效性。     

NONLINEAR DATA RECONCILIATION METHOD BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS

In the industrial process situation, principal component analysis (PCA) is a general method in data reconciliation. However, PCA sometime is unfeasible to nonlinear feature analysis and limited in application to nonlinear industrial process. Kernel PCA (KPCA) is extension of PCA and can be used for nonlinear feature analysis. A nonlinear data reconciliation method based on KPCA is proposed. The basic idea of this method is that firstly original data are mapped to high dimensional feature space by nonlinear function, and PCA is implemented in the feature space. Then nonlinear feature analysis is implemented and data are reconstructed by using the kernel. The data reconciliation method based on KPCA is applied to ternary distillation column. Simulation results show that this method can filter the noise in measurements of nonlinear process and reconciliated data can represent the true information of nonlinear process.     

A nonlinear quality-related fault detection approach based on modified kernel partial least squares

In this paper, a new nonlinear quality-related fault detection method is proposed based on kernel partial least squares (KPLS) model. To deal with the nonlinear characteristics among process variables, the proposed method maps these original variables into feature space in which the linear relationship between kernel matrix and output matrix is realized by means of KPLS. Then the kernel matrix is decomposed into two orthogonal parts by singular value decomposition (SVD) and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with relevant existing nonlinear approaches, the proposed method has the advantages of simple diagnosis logic and stable performance. A widely used literature example and an industrial process are used for the performance evaluation for the proposed method.     

基于KCCA虚假邻点判别的非线性变量选择

特征变量选择技术是非线性系统建模过程中降低信息冗余和提高精度的有效方法。提出一种结合核典型相关法(kernel canonical correlation analysis,KCCA)与虚假最近邻法的变量选择法。首先引入核方法,将非线性原始数据映射到线性空间,再采用典型相关法有效合理地消除因子之间的多重共线性,受混沌相空间虚假最近邻点法的启示,通过计算原始数据在KCCA子空间中投影的距离,判断其对主导变量的解释能力,由此进行变量的选择。该方法用氢氰酸生产工艺工程中的非线性模型验证,并与全参数模型进行比较,结果显示该方法有良好的变量选择能力。因此,该研究为非线性系统建模的变量选择方法提供了一种新方法。     

Statistical process monitoring based on orthogonal multi-manifold projections and a novel variable contribution analysis

Multivariate statistical methods have been widely applied to develop data-based process monitoring models. Recently, a multi-manifold projections (MMP) algorithm was proposed for modeling and monitoring chemical industrial processes, the MMP is an effective tool for preserving the global and local geometric structure of the original data space in the reduced feature subspace, but it does not provide orthogonal basis functions for data reconstruction. Recognition of this issue, an improved version of MMP algorithm named orthogonal MMP (OMMP) is formulated. Based on the OMMP model, a further processing step and a different monitoring index are proposed to model and monitor the variation in the residual subspace. Additionally, a novel variable contribution analysis is presented for fault diagnosis by integrating the nearest in-control neighbor calculation and reconstruction-based contribution analysis. The validity and superiority of the proposed fault detection and diagnosis strategy are then validated through case studies on the Tennessee Eastman benchmark process.     

基于KPCA的SBR过程监视

序批式反应器生化污水处理系统（SBR）具有复杂的生化反应机理，其固有的严重非线性、持续时间有限、非稳态运行等给其过程监视带来特殊困难。核主元分析（KPCA）方法通过集成算子与非线性核函数计算高维特性空间的主元成分，有效捕捉过程变量中的非线性关系。将KPCA技巧应用到序批式反应器生化污水处理系统，建立了基于KPCA的SBR污水处理过程在线监视策略。在监视暴风雨事件等典型的SBR过程异常状态时，统计指标变化灵敏，诊断及时。与线性PCA相比，显示出更高的过程监视性能。     

核函数主元分析及其在故障特征提取中的应用

提出了基于核函数主元分析的故障特征提取方法。该方法利用计算原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。通过对高维特征数据作主元分析，得到原始特征的非线性主元．以所选的非线性主元作为特征子空间，并应用转子试验台的故障数据对该方法进行了检验。结果表明，核函数主元分析更适于提取故障信号的非线性特征，它提取的故障特征对故障具有更好的识别能力，并对分类器具有较强的鲁棒性。     

基于KPCA子空间虚假邻点判别的非线性建模的变量选择

特征变量选择技术是非线性系统建模过程中降低信息冗余和提高精度的有效方法。提出一种结合核主成分分析法(Kernel principal components analysis,KPCA)与虚假最近邻点法(False nearest neighbor,FNN)的变量选择法。引入核方法,将非线性原始数据映射到线性空间,再采用主成分分析法有效合理地消除因子之间的多重共线性,受混沌相空间虚假最近邻点法的启示,通过计算原始数据在KPCA子空间中投影的距离,判断其对主导变量的解释能力,由此进行变量的选择该方法用氢氰酸生产工艺工程中的非线性模型验证,并与全参数模型进行比较,结果显示该方法有良好的变量选择能力。因此,该研究为非线性系统建模的变量选择方法提供一种新方法。     

核函数主元分析及其在齿轮故障诊断中的应用

提出了基于核函数主元分析的齿轮故障诊断方法。该方法通过计算齿轮振动信号原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。通过对高维特征数据作主元分析,得到原始特征的非线性主元,以所选的非线性主元作为特征子空间对齿轮工作状态进行分类识别。用齿轮在正常状态、裂纹状态和断齿状态下的试验数据对该方法进行了检验,比较了主元分析与核函数主元分析的分类效果。结果表明,核函数主元分析能有效的检测裂纹故障的出现,正确区分不同的故障模式,更适于提取故障信号的非线性特征。     

基于核函数PCA的齿轮箱状态监测研究

讨论核函数PCA(principal analysis component,主元分析)的算法原理,提出基于核函数PCA的齿轮箱状念监测方法。该方法借助于核函数计算得到原始特征的非线性主元,并根据非线性主元构建特征子空间,实现对齿轮箱运行状态的分类识别,并监测其状态变化。实例研究表明,核函数PCA分类效果比PCA好,能有效识别出齿轮箱的不同状念,并及时监测到齿轮箱工作状态的变化。     

Discriminant diffusion maps analysis: A robust manifold learner for dimensionality reduction and its applications in machine condition monitoring and fault diagnosis

Various features extracted from raw signals usually contain a large amount of redundant information which may impede the practical applications of machine condition monitoring and fault diagnosis. Hence, as a solution, dimensionality reduction is vital for machine condition monitoring. This paper presents a new technique for dimensionality reduction called the discriminant diffusion maps analysis (DDMA), which is implemented by integrating a discriminant kernel scheme into the framework of the diffusion maps. The effectiveness and robustness of DDMA are verified in three different experiments, including a pneumatic pressure regulator experiment, a rolling element bearing test, and an artificial noisy nonlinear test system, with empirical comparisons with both the linear and nonlinear methods of dimensionality reduction, such as principle components analysis (PCA), independent components analysis (ICA), linear discriminant analysis (LDA), kernel PCA, self-organizing maps (SOM), ISOMAP, diffusion maps (DM), Laplacian eigenmaps (LE), locally linear embedding (LLE) analysis, Hessian-based LLE analysis, and local tangent space alignment analysis (LTSA). Results show that DDMA is capable of effectively representing the high-dimensional data in a lower dimensional space while retaining most useful information. In addition, the low-dimensional features generated by DDMA are much better than those generated by most of other state-of-the-art techniques in different situations.     

核主元分析在透平机械状态监测中的应用

讨论了核主元分析(K erne l P rinc ipa l Com ponen t A na lys is,简称KPCA)原理,提出了基于KPCA的透平机械状态监测方法。该方法在低维特征空间利用内积核函数,实现原始空间到高维空间的非线性映射以及对高维映像数据的主元分析,从而在低维空间得到原始特征的非线性主元,并根据非线性主元构建特征子空间,实现特征提取和对透平机械状态的分类识别并监测其状态变化。对仿真数据及透平机械在正常、重负荷状态下试验数据的研究表明,KPCA分类效果比主元分析好,能有效地识别出透平机械的不同状态,并能及时监测到状态发生的变化。     

热力参数的KPCA-RBF网络建模及传感器故障诊断方法

针对复杂恶劣环境下机组热力参数的数据监测及传感器故障诊断问题,建立了融合机理分析、核主元分析(kernel principle component analysis,简称KPCA)与径向基神经网络(radial basis function,简称RBF)的发电机组热力参数预测及传感器故障检测模型。首先,根据机理分析得到完备的辅助变量集,并利用核主元分析提取辅助变量的特征信息以有效处理发电机组中高维、强耦合的非线性数据;其次,将主元变量集输入径向基神经网络进行学习,实现热力参数的重构;最后,基于预测模型与窗口移动法实现传感器的故障诊断,并对故障数据进行及时修复和准确替换。以燃气轮机排气温度为例进行验证的结果表明,该预测模型具有更高的精度和泛化能力,能在传感器故障发生初期及时发现并识别故障类型,检测效果优良。     

面向高光谱图像分类的空谱判别分析

针对传统的基于特征提取的高光谱图像地物分类算法大多只考虑光谱信息而忽略空间信息的问题,提出了一种面向高光谱分类的半监督空谱全局与局部判别分析(S3 GLDA)算法。该算法首先利用少量标记样本保存数据集的线性可分性和全局判别信息,再依靠较多的无标记的空间局部近邻像元来揭示局部判别信息和非线性局部流形,使高光谱遥感图像的光谱域全局判别结构和空间域局部判别结构在低维特征空间同时得以保留,并在输出特征中自动融入了空间信息,构成了半监督的空谱判别分析。在Indian Pines和PaviaU数据集的实验表明,总体分类精度分别达到76.24%和82.96%。与现有几种算法比较,该算法有效提高了输出特征在低维空间的判别能力,更好地揭示了数据集的内在非线性多模本质,有效提升了高光谱图像数据集的地物分类精度。     

冷轧过程断带故障的诊断研究

在冷轧过程中,断带故障是冷轧工序的主要生产故障之一。针对冷轧过程断带故障的特点,提出一种基于核主元分析(KPCA)非线性特征提取和最小二乘支持向量机(LSSVM)分类的故障诊断方法。此方法采用KPCA理论将冷轧过程原始空间数据映射到高维空间,并在高维空间进行主元分析,从而降维、去相关性,得到冷轧过程非线性特征向量。将降维后的特征主元作为LSSVM输入进行训练和识别,根据LSSVM的输出结果判断冷轧过程工作状态与故障类型。仿真结果表明:基于KPCA非线性特征提取和LSSVM分类的故障诊断方法计算速度快,能有效地提取冷轧过程断带故障特征,识别断带故障类型。