基于局部密度估计的多模态过程故障检测

以市场需求为导向的现代工业过程的生产条件要根据市场的需求不断做出调整,因此实际工业过程中存在多种工况的复杂情况,而过程的数据将不再完全服从高斯分布,其均值与协方差结构往往随着工况的切换而发生较大变化,为了能及时检测此类生产过程中的故障,提出一种新的基于带宽可变的局部密度估计的过程在线监控策略。首先利用局部投影保留(locality preserving projection,LPP)将高维数据投影到低维子空间中,充分地保留数据的局部结构;然后通过带宽可变的非参数密度核函数来进行局部密度估计,并采用局部密度因子(local density factor,LDF)的思想构造监控统计量,进而对工业过程故障进行在线检测;最后通过仿真研究,结果表明所提方法能够有效地应用于多模态过程的故障检测。     

基于非线性动态全局局部保留投影算法的化工过程故障检测

传统基于核映射的非线性故障检测方法的性能受核函数类型和核参数的调优影响较大，且实际工业环境中对过程变量的非线性阶数存在很多物理限制。针对这一问题，提出一种非线性动态全局局部保留投影（nonlinear dynamic global-local preserving projections，NDGLPP）的故障检测算法。该方法首先使用动态全局局部保留投影算法对数据矩阵进行降维；然后对降维后的矩阵建立二阶多项式映射提取非线性空间的相关特性；接着通过迭代这两个步骤以获得高阶非线性映射；最后，将所提方法应用于乙烯精馏过程和Tennessee Eastman（TE）过程仿真中，验证了检测方法的有效性和可行性。     

一种基于改进局部熵PCA的工业过程故障检测方法

针对工业过程的多模态和非高斯特性,提出一种基于改进局部熵主元分析(ILEPCA)的故障检测方法。引入k近邻的均值对局部概率密度函数进行改进,构造改进的局部熵数据剔除多模态和非高斯特性。对改进的局部熵数据建立主元分析(PCA)模型,根据核密度估计计算控制限。对于测试数据,运用改进的局部熵算法预处理后,向PCA模型上投影,计算统计量。通过比较统计量与控制限来进行故障检测。把该方法应用到数值例子和半导体过程故障检测,仿真结果表明,与PCA、核主元分析(KPCA)和局部熵PCA (LEPCA)相比,ILEPCA算法在具有多模态和非高斯特性的工业过程故障检测中具有明显的优越性。     

一种基于改进KICA的非高斯过程故障检测方法

针对基于核独立元分析(kernel independent component analysis,KICA)的故障检测方法只考虑非高斯信息提取而忽略局部近邻结构保持的问题,提出基于改进KICA的过程故障检测方法。将KICA法中只考虑非高斯信息提取的负熵最大化准则转换为熵最小化准则,结合局部保持投影的相似局部近邻结构准则,提出了同时考虑非高斯信息提取和局部近邻结构保持的目标函数,通过粒子群优化算法进行全局寻优,然后建立监控统计量对过程进行监控。在Tennessee Eastman过程上的仿真结果说明,与基于KICA的故障检测方法相比,所提方法能够在保持数据集局部近邻结构的同时,提取非高斯信息,能够有效缩短故障检测的延迟时间,提高故障检测率。     

基于核T-PLS的化工过程故障检测算法

针对全潜结构投影法（T-PLS）在检测非线性过程故障时误报率和漏报率较高的缺点,提出了基于核函数的全潜结构投影法（KT-PLS）。该算法通过核函数将过程数据从低维输入空间非线性地映射到高维特征空间,实现非线性问题的线性化;然后在质量变量的引导下将特征空间分为与质量直接相关、与质量正交、与质量无关和残差四个子空间;最后分别构建D和Q统计量进行故障检测。将该算法应用到Tennessee Eastman process（TEP）,多种故障模式下的仿真结果表明,KT-PLS比T-PLS更适合监控具有强非线性的生产过程。     

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。     

基于变量分组DTW-MCVA的不等长间歇过程故障检测方法

针对不等长间歇过程监控中批次数据同步化未能充分挖掘局部信息的问题,提出一种基于变量分组DTW-MCVA（VGDTW-CVA）的不等长间歇过程故障检测方法。首先,利用互信息矩阵描述不等长间歇过程测量变量之间的相关性,并基于互信息矩阵进行变量分组。然后利用DTW算法对各个变量组分别进行同步化,并将同步化后的变量组整合为完整的三维数据集。最后,利用MCVA方法建立动态监控模型实现对间歇生产过程的在线监控。盘尼西林发酵过程的仿真结果表明,VGDTW-MCVA能够比基本的DTW-MCVA方法更好地监控间歇过程故障。     

基于LSNPE算法的化工过程故障检测

复杂化工过程通常具有多个操作模态,而且采集的数据不服从单一的高斯或非高斯分布。针对化工过程的多模态和复杂数据分布问题,将局部标准化（local standardized,LS）策略应用于邻域保持嵌入（neighborhood preserving embedding,NPE）算法,提出了一种新的基于局部标准化邻域保持嵌入（local standardized neighborhood preserving embedding,LSNPE）算法的故障检测方法。首先,使用LSNPE算法提取高维数据的低维子流形,进行维数约减,同时保持邻域结构不变。其次,通过特征空间中样本的局部离群因子（local outlier factor,LOF）构造监控统计量并确定其控制限。相较于监控多模态化工过程的多模型策略,提出的LSNPE方法不需要过程先验知识的支持,只需建立一个全局的监控模型。最后,通过数值仿真及Tennessee Eastman（TE）过程仿真研究验证了本文提出方法的有效性。     

基于K均值聚类与局部离群因子算法的故障检测研究

针对化工生产过程的多工况、数据多模态问题,提出一种基于K均值聚类的局部离群因子故障检测方法。首先利用K均值聚类算法对多模态工业数据进行聚类,将各个模态的数据分离出来,然后运用局部离群因子算法在各个模态下单独建立模型,并且确定各个模态下的局部离群因子控制限。检测时首先判断样本属于哪一类,然后在相应类别下求取局部离群因子值并与此类别下的控制限进行比较,确定是否为故障数据。将此方法运用到TE过程的多模态数据中,并且将此方法与单独应用局部离群因子算法做故障检测对比,结果表明:所提算法可以大幅提高故障的检测率。     

基于LPP-GNMF算法的化工过程故障监测方法

提出了基于LPP-GNMF算法的化工过程故障监测方法。非负矩阵分解（NMF）是一种新兴的降维算法,由于它在机理上具有潜变量的正向纯加性的特点,所以在对数据进行压缩时,可以基于数据内部的局部特征有效描述数据信息,相比于传统的多元统计过程监控方法如主元分析（PCA）等有更好的解释能力。然而NMF要求原始数据满足非负性的要求,实际的化工过程有时并不能保证,为放宽对原始数据的非负要求,引入了广义非负矩阵分解（GNMF）算法。其次,GNMF在分解的过程中没有考虑到样本间的局部结构和几何性质,可能存在不能准确处理数据的问题。针对这一问题,提出了将GNMF与LPP（局部投影保留）相结合的算法。将提出的LPP-GNMF算法应用于TE过程来评估其监测性能,并与PCA算法、NMF算法、SNMF算法进行比较,仿真模拟结果表明所提算法的可行性。     

稀疏核局部保持投影及其在非线性过程故障检测中的应用(英文)

Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.     

Improved dynamic kernel PCA based on local preserving projections and its application for electric submersible pump fault diagnosis

Dynamic kernel principal component analysis (DKPCA) has been frequently implemented for nonlinear and dynamic process monitoring of complex industrial processes. However, traditional DKPCA focuses only on the global structural analysis of data sets and strongly neglects the local information, which is equally essential for process detection and identification. In this paper, an improved DKPCA, referred to as the local DKPCA (LDKPCA), is proposed based on local preserving projections (LPP) for nonlinear dynamic process fault diagnosis. The method combines the advantages of LPP and DKPCA by utilizing the local structure feature to maintain the geometric structure of the data in a unified framework. To achieve a highly comprehensive feature extraction, the local characteristics are fused in DKPCA to produce an optimization objective. The neighbouring points of the new objective function projection in the feature space are still maintained in proximity, and the variance information is retained simultaneously. For the purpose of fault detection, two statistics, known as the T² and squared prediction error (SPE) statistics, are constructed, based on the LDKPCA model, and used to monitor the latent variable space and the residual space, respectively. In addition, the sensitivity analysis is brought in for fault identification of the two statistics. Based on the experimental analysis using the shaft breakage data of an offshore oilfield electric submersible pump (ESP), the proposed method outperforms the conventional DKPCA in terms of fault monitoring performance. The experimental results demonstrate the potential of the method in nonlinear dynamic process fault diagnosis.     

Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Fault detection and identification are challenging tasks in chemical processes, the aimof which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis (PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model (each direction of principal components), but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.     

Multimode process monitoring strategy based on improved just-in-time-learning associated with locality preserving projections

In this paper, a multimode process monitoring strategy based on improved just-in-time-learning associated with locality preserving projections (IJITL-LPP) is proposed. First, raw data are projected into the feature space using locality preserving projections (LPP). Second, IJITL searches for similar samples of the query sample in the feature space by introducing a variational inference Gaussian mixture model (VIGMM). Finally, the new statistic named average distance is created to complete process monitoring. In the IJITL, the introduced VI can automatically determine the number of modes, thereby accelerating the efficiency of selecting similar samples. In the process monitoring phase, the average distance can reduce the impact of different mode dispersion on fault detection. In addition, LPP can render the model less sensitive to outliers. Compared with principal component analysis (PCA), LPP, K nearest neighbour rules, Gaussian mixture model (GMM), K-means based-PCA, and just-in-time-learning (JITL)-based LPP, the proposed method has better performance in a numerical case, the Tennessee Eastman process, and the semiconductor etching process.     

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     

Density peaks clustering-based steady/transition mode identification and monitoring of multimode processes

Multimode is the characteristic of industrial manufacturing processes due to different production strategies and environments. For multimode process monitoring, it is a challenge to identify different steady modes and transition modes. In this paper, a k nearest neighbours (KNN)-based density peaks clustering (DPC) method is applied to identify different modes. First, the local density of each sample, which is obtained with a KNN constraint and its minimum distance to the higher local density points are calculated as two indicators of the DPC algorithm to find the cluster centres of the training data. Then, the transition modes are identified by combining the moving window strategy and the DPC algorithm, where an index called the local density-distance ratio (LDDR) is employed. Finally, the monitoring algorithm is used to detect the faults for each operation mode. The effectiveness and advantages of the proposed method are illustrated by a numerical example and a Tennessee Eastman (TE) benchmark process.     

基于流程拓扑信息的统计过程监测方法

传统数据驱动的过程监测方法主要基于历史数据和统计学知识建立,往往忽视了对过程机理的考虑。基于预测残差的过程监测方法则通过数据驱动的回归模型实现对局部过程机理的近似,在预测残差的基础上建立监测模型实现了对过程偏离更好的识别。但其建立回归模型实现对局部过程机理的近似时主要基于数据,很少考虑具体流程信息。作为流程信息的一种表现形式,流程拓扑结构常被用来提取变量间的进程与因果关系,如果在建立回归模型时结合流程的拓扑结构,则可使得所建立的回归模型中包含一定的流程信息,使其对局部机理的近似更为准确。基于此,本文提出一种基于流程拓扑信息的统计过程监测方法。该方法利用流程的拓扑结构,提取变量间的进程与因果关系,建立回归模型实现对局部过程机理的近似。在此基础上建立基于预测残差的过程监测模型,实现对过程偏离的监测。该方法被应用于某连续重整装置的过程监测中,结果表明其监测效果要优于基于主元分析和基于预测残差的过程监测方法。