基于多工况混合模型的故障监控方法研究

针对多工况过程建立了一个多工况高斯混合模型(Gaussian Mixture Model,GMM),并利用EM(Expectation Maximization)算法对该GMM参数进行估计。通过引入贝叶斯阴阳算法(Bayesian YingYang,BYY),实现了GMM中混合工况数目的自动估计。然后,通过在所建GMM的每个分量中构建PCA模型,建立一个多工况故障监控混合模型。最后利用TE过程研究证明了所建模型在过程监控中的有效性。     

基于PCA混合模型的多工况过程监控

针对传统多元统计故障检测方法大多假设测量数据服从单一高斯分布的不足,提出了一种基于PCA(principal component analysis)混合模型的多工况过程监测方法。首先通过直接对混合模型的各高斯成分的协方差进行PCA降维变换,使得协方差阵对角化,既减少了运算量又避免了变量相关而导致的奇异性问题;同时采用BYY增量EM算法自动获取混合模型的最佳混合分量数目,避免了常规EM算法的不足。所得的混合模型,除包括均值、协方差和先验概率等参数外,还包括了PCA载荷阵,即对每个混合元建立了PCA模型。然后给出了统计量定义,实现对多工况过程的故障检测。数值例子和TE过程的应用表明,本文提出的方法无需过程先验知识,能自动获取工况数目、精确估计各个工况的统计特性,并更准确及时地检测出多工况过程的各种故障。     

基于ICA混合模型的多工况过程故障诊断方法

针对工业过程数据的多模态和非高斯特性,提出一种基于独立元混合模型(independent component analysis mixture model,ICAMM)的多工况过程故障诊断方法。该方法将独立元分析与贝叶斯估计结合,同时完成各个工况的数据聚类和模型参数求取,并建立基于贝叶斯框架下的集成监控统计量实时监控过程变化。在检测到故障后,针对传统的变量贡献图方法无法表征变量之间信息传递关系的缺点,提出基于信息传递贡献图的故障识别方法。该方法首先计算各变量对独立元混合模型统计量的贡献度,进一步通过最近邻传递熵描述故障变量之间的传递性,挖掘故障变量之间的因果关系,从而确定故障源变量和故障传播过程。最后对一个数值系统和连续搅拌反应釜(CSTR)过程进行仿真研究,结果验证了本文所提出方法的有效性。     

基于高斯混合模型与主元分析的多模型切换方法

针对多模型预测控制的模型切换问题,提出了一种基于工况判断的多模型切换方法,利用工业过程中的可测变量综合反映系统的动态特性,根据动态特性的变化进行多模型切换。首先利用高斯混合模型(GMM)将历史数据划分为若干个工况,然后利用不同工况下的历史数据建立负荷向量矩阵和预测模型,最后根据主元模型的平方预报误差(SPE)选择预测模型。以乙烯裂解炉的反应管出口温度(COT)的控制为例进行仿真,仿真结果表明:提出的方法实现了多个反应管出口温度的稳定均衡控制,当系统的工况发生改变时,通过不同主元模型的SPE统计量的比较,可以很容易地找到匹配的工况,并切换为相应的预测模型,解决了当系统动态特性发生改变时,预测模型切换滞后的问题。     

基于LECA的多工况过程故障检测方法

针对工业过程监控中的多工况复杂分布数据,提出一种基于局部熵成分分析(LECA)的故障检测方法。为处理数据的多模态分布问题,LECA首先采用KNN-Parzen窗方法估计变量的局部概率密度,进一步构造局部相对概率密度函数降低对窗参数选择的敏感性。为有效挖掘非高斯分布数据中的特征信息,利用信息熵理论计算过程数据的局部信息熵,并采用独立元分析(ICA)方法建立局部熵成分统计模型,实时检测过程故障。在数值例子和连续搅拌反应釜(CSTR)上的仿真结果表明,该方法在故障检测过程中能够获得较好的监控性能。     

多工况的DCPM-GA算法校正给水管网模型

通过建立管网仿真模型,模拟不同工况下的管网运行负荷情况,分析不同工况下的运行结果,在此基础上提出在不同工况下,以DCPM—GA算法对建立的仿真管网模型进行校正,通过实际工程验证发现,经过该法校核后的管网模型的计算结果与实测值较为接近,而且运行速度较快。     

基于测地线距离统计量的多工况间歇过程监测

针对间歇过程数据具有非线性和多工况的特点, 提出一种基于测地线距离统计量(geodesic distance statistic, GDS)的监测方法。首先, 对多工况间歇过程数据按批次方向展开及标准化, 利用主元分析(principal component analysis, PCA)方法进行降维;然后, 在降维空间获得赋权邻接矩阵, 提出采用改进的Dijkstra (improved Dijkstra, IDijkstra)算法使Dijkstra算法更易于实现, 计算各批次之间的测地线距离, 用以表征非线性多工况数据之间的实际最短距离, 更好地体现批次数据之间的局部近邻关系。通过构造测地线距离α次方统计量D^α进行过程监测, 与欧氏距离平方和D²相比将减小边缘训练数据距离的偏离程度。最后, 通过在数值仿真和工业仿真实例中的应用, 验证所提算法的有效性。     

一种新的多工况过程在线监测方法

针对复杂工业过程中的多工况和非高斯信息问题,提出一种基于外部分析的ICA-PCA（independent component analysis and principal component analysis）在线统计监测新方法。首先把过程变量分为外部变量和主要变量,通过偏最小二乘（PLS）回归方法分离外部变量对主要变量的影响,然后利用ICA-PCA两步信息提取策略,完整地提取过程的信息,最后用3个统计量对过程进行监测,建立了一种具有非高斯特性的多工况过程在线监测算法。通过对一个数值例子和连续搅拌槽（CSTR）过程的仿真研究,说明提出的方法是可行、有效的。     

多层翼形桨搅拌槽内混合过程的CFD模拟

The mixing process in a stirred tank of 0.476m diameter with single, dual and triple 3-narrow blade hydrofoil CBY impellers was numerically simulated by using computational fluid dynamics （CFD） package FLU-ENT6.1. The multi-reference frame （MRF） and standard k-ε turbulent model were used in the simulation. The shaft power and the mixing time predicted by CFD were in good agreement with the experiment. The effects of tracer feeding and detecting positions on mixing time were investigated. The results are of importance to the optimum design of industrial stirred tank/reactors.     

Nonstationary fault detection and diagnosis for multimode processes

Fault isolation based on data‐driven approaches usually assume the abnormal event data will be formed into a new operating region, measuring the differences between normal and faulty states to identify the faulty variables. In practice, operators intervene in processes when they are aware of abnormalities occurring. The process behavior is nonstationary, whereas the operators are trying to bring it back to normal states. Therefore, the faulty variables have to be located in the first place when the process leaves its normal operating regions. For an industrial process, multiple normal operations are common. On the basis of the assumption that the operating data follow a Gaussian distribution within an operating region, the Gaussian mixture model is employed to extract a series of operating modes from the historical process data. The local statistic T² and its normalized contribution chart have been derived for detecting abnormalities early and isolating faulty variables in this article. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

基于贝叶斯推理的PKPCAM的非线性多模态过程故障检测与诊断方法

针对一类非线性多模态的化工过程,提出一种基于概率核主元的混合模型(PKPCAM),并利用贝叶斯推理策略进行过程监控与故障诊断.在提出的模型中, 每个操作模态由一个局部化的概率核主元分量描述,从而构建的一系列分量对应了不同的操作模态.首先,将过程数据从原始的度量空间投影到高维特征空间;其次,在该特征空间建立概率主元混合模型,从概率角度刻画数据集的多个局部分量特征;最后,在提取的核主元分量内获得测试样本的后验概率,结合模态内的马氏距离贡献度,提出基于贝叶斯推理的全局概率指标进行故障检测,同时利用模态内变量的相对贡献度,基于全局贡献度指标进行故障诊断.利用TEP仿真平台,与基于k均值聚类的次级主元分析和核主元分析的方法进行了对比分析,验证了提出的贝叶斯推理的PKPCAM方法对非线性多模态过程进行故障检测与诊断的可行性和有效性.     

A new monitoring method for the between-mode transition of multimode processes

In practical industrial processes, the between-mode transition commonly exists. It is accepted that the transition has a closer association with its neighbouring modes than with the other modes in the multimode processes. Thus, this paper first separates the subspace based on the relationships between the transition and the features of its neighbouring modes. Then it models the local information of the transition in the remaining subspace. In this way, a complete monitoring structure is set up from cross-mode and inner-mode viewpoints. Moreover, the new monitoring method can be applied without special conditions. Both the three tank practical process and the Tennessee Eastman challenge problem have validated the effectiveness of the proposed method.     

多模态化工过程的全局监控策略

引言基于数据驱动的过程监控方法从20世纪80年代建立以来得到了蓬勃的发展,理论体系逐渐完善,功能模块不断丰富。特别是最近几年,来自人工智能,机器学习及信号处理领域的各种方法的引入为该领域注入了新的活力。目前,多数基于数据     

Multiway independent component analysis mixture model and mutual information based fault detection and diagnosis approach of multiphase batch processes

Batch process monitoring is a challenging task, because conventional methods are not well suited to handle the inherent multiphase operation. In this study, a novel multiway independent component analysis (MICA) mixture model and mutual information based fault detection and diagnosis approach is proposed. The multiple operating phases in batch processes are characterized by non‐Gaussian independent component mixture models. Then, the posterior probability of the monitored sample is maximized to identify the operating phase that the sample belongs to, and, thus, the localized MICA model is developed for process fault detection. Moreover, the detected faulty samples are projected onto the residual subspace, and the mutual information based non‐Gaussian contribution index is established to evaluate the statistical dependency between the projection and the measurement along each process variable. Such contribution index is used to diagnose the major faulty variables responsible for process abnormalities. The effectiveness of the proposed approach is demonstrated using the fed‐batch penicillin fermentation process, and the results are compared to those of the multiway principal component analysis mixture model and regular MICA method. The case study demonstrates that the proposed approach is able to detect the abnormal events over different phases as well as diagnose the faulty variables with high accuracy. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2761–2779, 2013     

GMM and optimal principal components-based Bayesian method for multimode fault diagnosis

Principal component analysis (PCA) serves as the most fundamental technique in multivariate statistical process monitoring. However, other than determining contributions to a fault from each variable based on the pre-selected major principal components (PCs), the PCA-based fault diagnosis with an optimal selection of PCs is seldom investigated. This paper presents a novel Gaussian mixture model (GMM) and optimal principal components (OPCs)-based Bayesian method for efficient multimode fault diagnosis. First, the GMM and Bayesian inference is utilized to identify the operating mode, and then local PCA model is established in each mode. Second, given that the various principal components (PCs) may contain distinct fault signatures, the behavior of each PC in local PCA is examined and the OPCs are selected through stochastic optimization algorithm. Based on the OPCs, a Bayesian diagnosis system is then formulated to identify the fault statuses in a probability manner. Performance of GMM–OPC Bayesian diagnosis is examined through a numerical example and the Tennessee Eastman challenge process. The efficiency and feasibility are demonstrated.     

一种不等长的多模态间歇过程故障检测方法

提出一种不等长的多模态间歇过程故障检测方法。首先,运用局部加权算法对不等长批次数据进行预处理。在训练样本中确定不等长数据的最大可保留长度,利用k近邻信息,通过加权重构出不等长批次缺失的数据点。其次,对等长的训练集构造局部近邻标准化矩阵,运用K-means算法进行模态聚类,使用局部离群因子方法确定第一控制限,并剔除离群样本。最后,对各个模态建立MPCA模型并确定第二控制限。根据各个模态控制限的匹配系数计算统一的统计量和控制限,在统一的控制限下进行多模态故障检测。将提出方法应用于半导体工业过程,仿真结果表明,与传统的故障检测算法相比,本文算法提高了故障检测率,验证了该方法的有效性。     

独立分量分析技术在设备故障诊断中的应用

针对设备振动信号复杂难以分离的特点,提出采用独立分量分析技术对多源振动混合信号进行分离降噪和特征提取。实验结果表明,利用该方法可有效对多源信号进行分离降噪,提取特征信号,从而达到提高故障诊断准确率的目的。