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。     

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。     

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.     

基于联合典型变量矩阵的多阶段发酵过程质量相关故障监测

为考虑发酵过程的质量变量和动态特征对于阶段划分的影响,提出了一种基于联合典型变量矩阵的多阶段发酵过程质量相关故障监测方法。首先,将历史三维数据沿批次方向展开,对每个时间片矩阵进行典型相关分析（canonical correlation analysis, CCA）,得到融合过程变量和质量变量信息的联合典型变量矩阵,对其进行K均值聚类,实现基于静态特征的第1步划分;然后采用慢特征分析（slow feature analysis, SFA）算法提取表征过程动态性的慢特征,对其进行聚类实现第2步划分。最后综合分析两步划分结果,将生产过程划分为不同的稳定阶段和过渡阶段,并在划分的子阶段中分别建立CCA监测模型进行质量相关故障监测。该方法通过静态和动态特征的变化实现两步划分,准确区分强动态变化与阶段切换,有效提高质量相关的故障监测模型精度。青霉素仿真平台与大肠杆菌实际生产数据验证了所提方法的可行性和有效性。     

基于关联规则的条件状态模糊Petri网及其在故障诊断中的应用

模糊Petri网作为一种知识表达模型,能够用于工业过程系统故障推理和诊断。然而,模糊Petri网的建立大多需要先验知识,为此限制了其广泛应用。为了能够有效利用工业生产过程数据,提出了一种基于关联规则的条件状态模糊Petri网,并将其用于工业过程故障推理与诊断。采用数据挖掘的关联规则方法提取模糊Petri网的模糊规则及置信度,通过变量间的关联分析,将影响置信度的关键主元（条件量）提取出来,建立条件状态模糊Petri网;基于极大代数的迭代算法进行动态置信度逆向推理,可以获得工业过程的故障发生概率。该方法实现了故障诊断网络的数据驱动,从而提高故障诊断的快速性与准确性,某化学反应研究表明所提方法的有效性。     

基于互信息的分散式动态PCA故障检测方法

对现代大型复杂动态过程来讲,不同测量变量会存在不同的序列相关性,而且变量间的相互影响会体现在不同的采样时刻上。为此,结合利用分散式建模的优势,提出一种基于互信息的分散式动态过程故障检测方法。该方法在对每个测量变量都引入多个延时测量值后,利用互信息为每个变量区分出与其相关的测量值,并建立起相应的变量子块。这种变量分块方式使每个变量子块都能充分地获取与之相对应的自相关性与交叉相关性信息,较好地处理了数据的动态性问题。然后,利用主元分析（PCA）算法对每一变量子块进行统计建模从而建立起适于大规模动态过程的多模块化的故障检测模型。最后,通过实例验证该方法用于动态过程监测的可行性和有效性。     

A local and global statistics pattern analysis method and its application to process fault identification☆

Traditional principal component analysis (PCA) is a second-order method and lacks the ability to provide higher-order representations for data variables. Recently, a statistics pattern analysis (SPA) framework has been incor-porated into PCA model to make full use of various statistics of data variables effectively. However, these methods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis (LGSPA) method, which integrates SPA framework and locality pre-serving projections within the PCA, is proposed to utilize various statistics and preserve both local and global in-formation in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution (IRBC) plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables' statistics, a new contribution of process variables is built to locate fault variables. The simula-tion results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.     

Dynamic simulation based fault detection and diagnosis for distillation column

The model-based fault diagnosis approach is characterized by a powerful process supervision capability with a priori knowledge about the system under consideration. Nevertheless, system complexity, high dimensionality, process nonlinearity and/or lack of good data often hamper its application in chemical engineering systems. A nonsteady state model based fault detection and diagnosis method for the distillation process was developed, using dynamic simulation to monitor the distillation process and identify abnormal sources when large deviations among measuring variables occur. It continuously updates the inner distillation parameters via on-line correction and predicts the trend of measuring variables and determines the existence of malfunctions simultaneously. The distillation model is dependent on transfer equilibrium, mass and heat balance, and is simulated by Euler and two-tier approach. This method was demonstrated with simulated data of a stripping tower collected from the Tennessee Eastman chemical plant simulator.     

An extended logical analysis of data approach to fault detections of industrial hybrid systems

It is difficult to deal with industrial hybrid systems involving both continuous and discrete variables using conventional data-driven fault detection methods. While logical analysis of data (LAD) methods are able to effectively explore hidden rules in discrete and continuous data by means of logical analysis for variable associations. However, conventional LAD has the problem of losing trend change information when extracting features of continuous variables. And when processing industrial data with high-dimensional, multivariate features, it will cause a lot of redundancy in the extracted rules. Motivated by these observations, this paper presents an extended logical analysis of data (ELAD) approach to fault detections of industrial hybrid systems. Therein, correlated variables are selected according to the association degree with key variables and additive variable trends are employed to characterize process status changes, creating an explicable fault detection model. The proposed method is applied to the steam drum process of an industrial coal gasification plant in detecting the influence of key hybrid variables on the fault of steam drum level. The results verify the feasibility and effectiveness of the contribution.     

面向工业混杂系统故障检测的扩展数据逻辑分析方法

常规的数据驱动故障检测方法难以处理同时包含连续和离散变量的工业混杂系统,数据逻辑分析（logical analysis of data, LAD）方法通过对历史数据中变量组合的逻辑分析,能够有效地挖掘离散和连续变量数据中存在的隐含规则。然而,常规的LAD在提取连续变量特征时存在对趋势变化信息丢失的问题,并且在处理具有高维度、多变量特征的工业数据时会导致提取的规则存在大量冗余。为此,本文提出一种基于扩展数据逻辑分析（extended logical analysis of data, ELAD）的工业混杂系统故障检测方法,根据与关键变量的关联度选取相关变量,增加变量的趋势信息以进行过程状态变化的表征,生成可解释的故障检测模型。应用于工业煤气化汽包过程,有效地检测了关键混杂变量对汽包液位故障的影响,实验结果验证了所提方法的可行性和有效性。     

A novel dynamic bayesian network‐based networked process monitoring approach for fault detection,propagation identification,and root cause diagnosis

A novel networked process monitoring, fault propagation identification, and root cause diagnosis approach is developed in this study. First, process network structure is determined from prior process knowledge and analysis. The network model parameters including the conditional probability density functions of different nodes are then estimated from process operating data to characterize the causal relationships among the monitored variables. Subsequently, the Bayesian inference‐based abnormality likelihood index is proposed to detect abnormal events in chemical processes. After the process fault is detected, the novel dynamic Bayesian probability and contribution indices are further developed from the transitional probabilities of monitored variables to identify the major faulty effect variables with significant upsets. With the dynamic Bayesian contribution index, the statistical inference rules are, thus, designed to search for the fault propagation pathways from the downstream backwards to the upstream process. In this way, the ending nodes in the identified propagation pathways can be captured as the root cause variables of process faults. Meanwhile, the identified fault propagation sequence provides an in‐depth understanding as to the interactive effects of faults throughout the processes. The proposed approach is demonstrated using the illustrative continuous stirred tank reactor system and the Tennessee Eastman chemical process with the fault propagation identification results compared against those of the transfer entropy‐based monitoring method. The results show that the novel networked process monitoring and diagnosis approach can accurately detect abnormal events, identify the fault propagation pathways, and diagnose the root cause variables. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2348–2365, 2013     

Fault prediction of non-linear multi-mode industrial process based on MKPCA model

The actual industrial process is regarded as a non-linear multi-mode process. If the system is described according to the model of the single-mode or the linear multi-mode, it will inevitably lead to a large number of false alarms or missed alarms. The fault detection, fault reconstruction, fault amplitude estimation, and prediction for non-linear multi-mode process based on the multi-kernel principal component analysis (MKPCA) model are studied in this paper. First, the MKPCA model will be applied to fault detection in the steady-state process of different modes, and a weighted algorithm will be adopted for fault detection in the transition process. Then, the fault degree will be described quantitatively, and the fault amplitude in the form of fault reconstruction will be solved by the optimization method, and the consistent estimation algorithm of fault amplitude under different modes will also be studied. Finally, the prediction model of support vector machine (SVM) prediction model will be applied to predict the development trend of the fault amplitude. Furthermore, the Tennessee Eastman (TE) process will be taken as an application object to verify the effectiveness and superiority of the method.     

基于模拟疫苗的人工免疫系统在氯乙烯聚合间歇过程故障诊断中的应用

根据聚氯乙烯工业生产过程的工艺信息、操作规程和历史数据,利用Aspen软件建立了氯乙烯悬浮聚合间歇过程的动态仿真模型,由模拟得到的多个状态的样本数据生成正常模拟疫苗和故障模拟疫苗,结合工业数据建立正常抗体库和故障抗体库,解决了氯乙烯聚合过程故障诊断中故障样本数据缺乏的问题。利用动态时间弯曲算法和人工免疫系统对该聚合反应间歇过程进行故障诊断,具有较好的诊断效果。     

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。     

A novel fault classification method using reconstructed distance-based discriminant locality preserving projection for industrial processes

Fault detection and classification is a crucial issue in modern industrial processes for ensuring steady operation and high product quality. The process data collected and stored fully reflect the equipment running state and the production process. Moreover, the extracted nonlinear features can directly affect the effectiveness of the data-driven fault classification model. In this paper, a novel fault classification method based on nonlinear feature extraction using reconstructed distance-based discriminant locality preserving projection (RD-DLPP) is proposed. First, a hypersphere model for each class of data is developed according to the spatial structures and classes information in high-dimensional space. The hyperspheres are used as indicators to evaluate the discriminatory difficulty of samples. Second, the constraints of the correlations between the k-nearest neighbour points of the sample and the hypersphere are introduced, which can efficiently reconstruct new measure metrics between the sample and its k-nearest neighbour points. Finally, an improved fault classification model based on RD-DLPP is established for the construction of the highly discriminant subspace. The Bayesian decision is then used to classify the samples. The feasibility and efficiency of the proposed method are verified by the Tennessee Eastman process as a case study.     

Deep neural network based recursive feature learning for nonlinear dynamic process monitoring

The data collected from modern industrial processes always have nonlinear and dynamic characteristics. The recently developed deep neural network method, stacked denoising auto-encoder (SDAE), can extract robust nonlinear latent variables from data against noise. However, it leaves the dynamic relationship unconsidered. To solve this problem, a novel algorithm named the recursive stacked denoising auto-encoder (RSDAE) is proposed. To learn the dynamic relationship, the RSDAE focuses on the predictability of the latent variables in the recurrence to contain the most dynamic variations. After the dynamic variations are extracted by the RSDAE, there is little autocorrelation left in the residuals. Then, the residuals can be monitored by principal component analysis (PCA). For the purpose of process monitoring, corresponding fault detection statistics are developed based on the RSDAE. Finally, a numerical case and the Tennessee Eastman process benchmark are used to demonstrate the effectiveness of the proposed algorithm.     

基于加权统计特征KICA的故障检测与诊断方法

针对核独立元分析（kernel independent component analysis, KICA）在非线性动态过程中对微小故障检测率低的问题,提出一种基于加权统计特征KICA（weighted statistical feature KICA, WSFKICA）的故障检测与诊断方法。首先,利用KICA从原始数据中捕获独立元数据和残差数据;然后,通过加权统计特征和滑动窗口获取改进统计特征数据集,并由此数据集构建统计量进行故障检测;最后,利用基于变量贡献图的方法进行过程故障诊断。与传统KICA统计量相比,所提方法的统计量对非线性动态过程中的微小故障具有更高的故障检测性能。应用该方法对一个数值例子和田纳西-伊斯曼（Tennessee-Eastman, TE）过程进行仿真测试,仿真结果显示出所提方法相对于独立元分析（ICA）、KICA、核主成分分析（kernel principal component analysis, KPCA）和统计局部核主成分分析（statistical local kernel principal component analysis, SLKPCA）检测的优势。     

基于MAF的传感器故障检测与诊断

针对工业控制系统中变量之间既存在线性相关性,且在时间结构上呈现自相关的特点,提出了一种基于最小/最大自相关因子(min/max autocorrelation factors, MAF)分析的传感器故障检测与诊断方法。首先,利用正常工况下的历史数据进行自相关因子分析,获得强自相关因子和弱自相关因子;在此基础上构造故障检测统计量,由核密度估计方法获得故障检测控制限,根据贡献图进行传感器故障定位。将所提出的方法应用于连续反应釜仿真过程的传感器故障检测与诊断,与经典的多变量统计方法——主元分析方法相比,所提出的方法能避免虚警,更快地检测缓变故障,并能更好地诊断和解释复杂故障。     

Applicability domains and accuracy of prediction of soft sensor models

Soft sensors are used to estimate process variables that are difficult to measure online. However, the predictive accuracy gradually decreases with changes in the state of chemical plants. Regression models can be updated, but if the model is updated with abnormal data, the predictive ability deteriorates. In practice, when the prediction error of an objective variable exceeds a threshold, an abnormal situation is detected. However, no effective method exists to decide this threshold. We have proposed a method to estimate the relationships between applicability domains and the accuracy of prediction of soft sensor models quantitatively. The larger the distances to models (DMs), the lower the estimated accuracy of prediction. Hence, the model between DMs and accuracy can separate variations in process variables and y‐analyzer fault. This method was applied to real industrial data. The fault detection ability of the proposed method was better than that of the traditional one. © 2010 American Institute of Chemical Engineers AIChE J, 2011