Bayesian filtering of the smearing effect: Fault isolation in chemical process monitoring

The isolation of faulty variables is a crucial step in the determination of the root causes of a process fault. Contribution plots, with their corresponding control limits, are the most popular tools used for isolating faulty variables. However, the isolation results may be misled by the smearing effect. In addition, the control limits of the contributions cannot be used to isolate faulty variables, as the control limits are obtained from normal operating data, which lack any information about the faults. In chemical processes, process faults rarely show random behavior; on the contrary, they will be propagated to different variables due to the actions of the process controllers. During the evolution of a fault, the task of isolating faulty variables needs to be concerned with the faulty variables identified at a previous time-point; in addition, the current decisions should influence the isolation results for the next sample when a fault constantly occurs. In the presented work, an unsupervised data-driven fault isolation method was developed based on Bayesian decision theory. Two fault scenarios of the Tennessee Eastman (TE) process were illustrated using visual comparative analysis to demonstrate how the different faulty variables were isolated when the fault evolved. In the industrial application, the proposed approach successfully located the faulty variables that were individually responsible for the simultaneous occurrence of multiple sensor faults and a process fault.     

基于多尺度核主元分析的非线性过程监控方法研究

针对化工过程数据的多尺度性和非线性特性,提出了一种多尺度核主元分析方法(MSKPCA)监控过程的运行状态。使用小波变换在不同尺度下分解测量信号．然后借助于核函数对分解后的数据进行非线性变换,在变换后的线性空间中用主元分析(PCA)提取过程数据的主要特征,构造监控统计量T2和Q来检测故障。在此基础上,提出了一种贡献图方法．计算过程变量对故障的贡献量,用于故障变量的分离。在TE过程上的监控结果表明,MSKPCA可以比PCA和动态PCA更迅速地检测到过程故障,贡献图方法能够正确地分离故障变量。     

加权k最近邻重构分析的工业过程故障诊断

k--最近邻(k--nearest neighbor, k--NN)是一种有效的基于数据驱动的故障检测方法, 该方法在工业过程监视方面已经得到了广泛的应用. 但在过程中存在故障时, 精确地寻找故障根源和识别故障变量是故障诊断的重要目标, 也是保证工业过程安全生产的重要任务. 本文在k--NN故障检测技术的基础上, 提出了一种加权的k--NN重构方法, 对使控制指标减小最大(maximize reduce index, MRI)的过程变量依次进行重构, 进而确定发生故障的传感器. 根据理论分析并结合数值仿真对提出的方法进行了验证, 数值仿真先从精度方面验证了该方法能够有效地对故障传感器数值进行重构, 然后验证了该方法不仅适用于单一传感器 故障诊断, 对于同时发生或者因变量相关性而传播的传感器故障也具有很好的效果. 最后, 该方法被成功应用于TE(Tennessee Eastman)化工过程.     

基于T-PLS贡献图方法的故障诊断技术

多变量统计过程监控对于复杂工业过程是一种有效的故障检测和诊断技术. 最小二乘(或称潜空间投影)模型是多变量统计过程监控中常用的一种投影模型, 能够同时对过程数据和质量数据进行建模. 讨论了一种新的基于全潜空间投影模型的故障诊断技术. 全潜空间投影模型中有4个检测统计量. 提出了一种新的T2贡献图计算方法, 对于所有检测统计量, 得到了相应的贡献图算法. 为了确定一个变量是否发生了故障, 计算所有变量贡献图的控制限. 该技术可以将辨识到的故障变量分为与Y有关和与Y无关的两类. 基于Tennessee Eastman过程的案例研究表明了该技术的有效性.     

基于核直接分解的过热蒸汽系统故障诊断方法

为提高过热蒸汽系统的运行效率并减少非紧要故障的报警率,本文提出一种质量相关的非线性故障检测与诊断方法.首先,利用核函数将过程变量映射到高维特征空间以消除原始变量之间的非线性耦合.然后,在特征空间进行核直接分解得到两个正交子空间,并在两个子空间中分别设计统计量指标进行质量相关的故障检测.在此基础上,利用偏微分贡献图提取每个变量对联合统计量指标的贡献率,并根据贡献率大小最终确定故障变量.仿真结果表明,所提出的方法能够准确区分影响过热蒸汽温度和不影响过热蒸汽温度的故障,有效降低了非紧要故障的报警率,提高了过热蒸汽系统的运行效率.     

一种基于独立元贡献度的子空间故障检测方法

针对工业过程故障检测问题,本文定义了独立元贡献度和贡献度矩阵,提出一种改进的子空间检测算法.首先,利用独立元分析(independent component analysis,ICA)算法提取过程变量的独立元信息,通过计算各个独立元在过程变量上的贡献度,构造贡献度矩阵;然后根据贡献度的大小,挑选出对应的变量组成反映不同"源"信息的子空间,并在这些子空间上建立故障检测模型;最后综合以上的多个检测模型,根据实际的需求或者故障的传播特征,确定集成策略,对工业过程进行故障检测.通过在TE(Tennessee Eastman)过程上对21种故障工况和1种正常工况的仿真研究,说明提出的改进算法是有效的.     

Probabilistic contribution analysis for statistical process monitoring: A missing variable approach

Probabilistic models, including probabilistic principal component analysis (PPCA) and PPCA mixture models, have been successfully applied to statistical process monitoring. This paper reviews these two models and discusses some implementation issues that provide alternative perspective on their application to process monitoring. Then a probabilistic contribution analysis method, based on the concept of missing variable, is proposed to facilitate the diagnosis of the source behind the detected process faults. The contribution analysis technique is demonstrated through its application to both PPCA and PPCA mixture models for the monitoring of two industrial processes. The results suggest that the proposed method in conjunction with PPCA model can reduce the ambiguity with regard to identifying the process variables that contribute to process faults. More importantly it provides a fault identification approach for PPCA mixture model where conventional contribution analysis is not applicable.     

Efficient faulty variable selection and parsimonious reconstruction modelling for fault isolation

Reconstruction-based fault isolation, which explores the underlying fault characteristics and uses them to isolate the cause of the fault, has attracted special attention. However, it does not explore how the specific process variables change and which ones are most significantly disturbed under the influences of abnormality; thus, it may not be helpful to understanding the specifics of the fault process. In the present work, an efficient faulty variable selection algorithm is proposed that can detect the significant faulty variables that cover the most common fault effects and thus significantly contribute to fault monitoring. They are distinguished from the general variables that are deemed to follow normal rules and thus are uninformative to reveal fault effects. To further reveal the fault characteristics, the selected significant faulty variables are then chosen to obtain a parsimonious reconstruction model for fault isolation in which relative analysis is performed on these selected faulty variables to explore the relative changes from normal to fault condition. The faulty variable selection can not only focus more on the responsible variables but also exclude the influences of uninformative variables and thus probe more effectively into fault effects. It can also help in finding a more interesting and reliable model representation and better identify the underlying fault information. Its feasibility is illustrated with simulated faults using data from the Tennessee Eastman (TE) benchmark process.     

基于精简并发潜结构映射的竖炉焙烧过程综合故障诊断

竖炉焙烧过程因运行条件异常变化或操作不当会造成上火、冒火、过还原和欠还原等运行故障.这些故障直接影响过程运行安全和产品质量（比如,磁选管回收率）,但难以采用基于模型和基于知识的方法建模故障与产品质量的关系,以及诊断故障变量.针对上述问题,本文提出数据驱动的基于并发潜结构映射（Concurrent projection to latent structures,CPLS）的竖炉焙烧过程综合故障诊断方法.首先,将并发潜结构映射分解的过程变量共有子空间与残差空间精简合并来建立磁选管回收率相关的过程变化空间,提出基于精简并发潜结构映射模型的竖炉焙烧过程综合监控方法;接下来,定义相应的重构贡献图并与竖炉焙烧过程相结合,提出CPLS精简重构贡献方法用于竖炉焙烧过程故障变量诊断;最后,利用竖炉焙烧过程半实物仿真平台采集的数据进行实验研究,结果表明所提方法不仅可以诊断出质量相关的故障,而且可诊断出回路设定值之外的故障变量.     

Fault diagnosis of continuous annealing processes using a reconstruction-based method

The continuous annealing process line (CAPL) has complex process characteristics, such as strong correlation of a large number of process variables and interconnected multi-subsystems and multiple operation zones. Practitioners are concerned with typical process faults, such as strip-break and roll-slippage, whose effects are often confined in a specific zone. Considering the large-scale process characteristics and fault characteristics, a multi-block fault diagnosis method is proposed. A novel reconstruction-based block contribution (RBBC) is first proposed in order to diagnose the faulty block. The reconstruction-based variable contribution (RBVC) within a block is also proposed to determine the faulty variables. The proposed RBBC-RBVC hierarchical scheme is applied successfully to a real CAPL on two fault cases. A finite state machine is utilized to diagnose strip-break and reconstructed combined index is studied to diagnose roll-slippage.     

Fault diagnosis with multivariate statistical models part I: using steady state fault signatures

Multivariate statistical approaches to fault detection based on historical operating data have been found to be useful with processes having a large number of measured variables and when causal models are unavailable. For fault isolation or diagnosis they have been less powerful because of the non-causal nature of the data on which they are based. To improve the fault isolation with these methods, additional data on past faults have been used to supplement the models. A critical review of this fault isolation literature is given, and an improved approach capable of handling both simple and complex faults is presented. This approach extracts fault signatures that are vectors of movement of the fault in both the model space and the residual space. The directions of these vectors are then compared to the corresponding vector directions of known faults in the fault library. Isolation is then based on a joint plot of the angles between the vectors of the current fault and those of the known faults. Although the fault signatures are based on steady-state information, the methodology assumes that time varying disturbances due to common-cause sources are always present, and it is applied to dynamic data as soon as a fault is detected. The method is demonstrated using a simulated CSTR system with feedback control, and is shown to be effective in isolating both simple and complex faults.     

基于分块核主成分分析和支持向量机的故障检测

针对工业系统监测数据为非线性,且难以辨识复杂工作过程中故障位置的问题,提出一种基于分块核主成分分析(BKPCA)和最小二乘支持向量机(LS-SVM)的集成故障检测方法.首先对系统监测变量进行分块,使用KPCA对每个分块在特征空间中建立T2和平方预测误差(SPE)统计量来实时监测系统健康状态,并使用LS-SVM对上述过程检测出来的故障数据进行再次判断.随后计算出现故障后计算每一分块的故障贡献率,进而确定发生故障的分块.由于采用了并行分块算法,可以较简单的确定故障发生位置,提高计算效率,同时LS-SVM方法的应用也可以提升故障检测的精度.使用田纳西-伊斯曼化工(TE)过程数据对本文所提方法进行仿真验证,试验结果表明所提方法取得了较好效果.     

Nonlinear Gaussian Belief Network based fault diagnosis for industrial processes

A Nonlinear Gaussian Belief Network (NLGBN) based fault diagnosis technique is proposed for industrial processes. In this study, a three-layer NLGBN is constructed and trained to extract useful features from noisy process data. The nonlinear relationships between the process variables and the latent variables are modelled by a set of sigmoidal functions. To take into account the noisy nature of the data, model variances are also introduced to both the process variables and the latent variables. The three-layer NLGBN is first trained with normal process data using a variational Expectation and Maximization algorithm. During real-time monitoring, the online process data samples are used to update the posterior mean of the top-layer latent variable. The absolute gradient denoted as G-index to update the posterior mean is monitored for fault detection. A multivariate contribution plot is also generated based on the G-index for fault diagnosis. The NLGBN-based technique is verified using two case studies. The results demonstrate that the proposed technique outperforms the conventional nonlinear techniques such as KPCA, KICA, SPA, and Moving Window KPCA.     

Generalized grouped contributions for hierarchical fault diagnosis with group Lasso

In process industries, it is necessary to conduct fault diagnosis after abnormality is found, with the aim to identify root cause variables and further provide instructive information for maintenance. Contribution plots along with multivariate statistical process monitoring are standard tools towards this goal, which, however, suffer from the smearing effect and high diagnostic complexity on large-scale processes. In fact, process variables tend to be naturally grouped, and in this work, a novel fault identification strategy based on group Lasso penalty along with a hierarchical fault diagnosis scheme is proposed by leveraging group information among variables. By introducing the group Lasso as a regularization approach, groups of irrelevant variables tend to yield exactly zero contributions collectively, which help find the exact root cause, alleviate the smearing effect, and furnish clear diagnostic information for process practitioners. For online computational convenience, an efficient numerical solution strategy is also presented. Besides, it turns out that the proposed approach also applies to dynamic monitoring models with lagged measurements augmented, thereby enjoying widespread generality. Its effectiveness is evaluated on both the Tennessee Eastman benchmark process and a pilot-scale experiment apparatus.     

Active fault tolerant control using state-space self-tuning control approach

This paper presents a new fault tolerant control scheme for unknown multivariable stochastic systems by modifying the conventional state-space self-tuning control approach. For the detection of faults, a quantitative criterion is developed by comparing the innovation process errors occurring in the Kalman filter estimation algorithm, which, for faulty system recovery, a weighting matrix resetting technique is developed by adjusting and resetting the covariance matrices of the parameter estimate obtained in the Kalman filter estimation algorithm to improve the parameter estimation of the faulty systems. The proposed method can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection. The modified state-space self-tuning control scheme can be applied to the multivariable stochastic faulty system without requiring prior knowledge of system parameters and noise properties.     

基于PCA的重油分馏塔故障监测与诊断分析

对系统过程数据进行主元分析,建立主元模型,可以在保留原有数据信息特征的基础上消除变量关联和部分系统噪声干扰,从而简化系统分析的复杂度。建立正确的主元模型,结合多变量统计过程控制图(Q统计图,HotellingT2图,主元得分图,贡献图),是对过程对象的进行检测和诊断的一项发展中的技术。通过对一个典型的重油分馏塔运行过程的故障监测与诊断分析,进一步说明了主元模型在确定故障特征方向和多变量统计控制图在监测和诊断故障源上的作用和有效性。同时采用了平均贡献图来直观明确地判别引起系统故障的主要原因。     

Reference governor design for tracking problems with fault detection guarantees

The present paper deals with the reference tracking problem for processes with linear dynamics and multisensor information subject to abrupt sensor faults. A key point for fault tolerance will be the separation between healthy and faulty closed-loop behavior upon a set-characterization approach. This is achieved through set theoretic operations involving the healthy/faulty behavior of residual signals related to the system dynamics. As a main contribution, a reference governor scheme is designed using a receding horizon technique. It is shown that fault detection guarantees can be achieved by appropriate adjusting of the governor's delay/prediction window under mild assumptions on the fault scenario.     

Sensor validation and process fault diagnosis for FCC units under MPC feedback

Model predictive control (MPC) has been widely applied in industry, especially in the refining industry. As all feedback controllers require correct sensor measurements, unreliable sensors can cause the MPC controller to move the process in an erroneous manner. Data validation of sensor measurements is a prerequisite in applying advanced control, particularly multivariable control which depends on many sensors. However, little research work is available on how feedback controllers like MPC complicate the task of sensor validation and process fault diagnosis. In theory, a controller can transfer the effect of a sensor fault in a controlled variable to the manipulated variables. In this paper, principal component analysis (PCA) is applied to detect, identify and reconstruct faulty sensors in a simulated FCC unit. A base PCA model is generated by perturbing the process throughout the operating region. Performance of MPC with and without data validation is compared. The same base PCA model is applied to detect and identify dynamic process faults. We demonstrate that process faults can be detected and diagnosed at an early stage.     

基于广义主成分分析的重构故障子空间建模方法

在线故障诊断是工业过程中十分重要的问题.相比传统贡献图而言,基于重构的故障诊断受到特别关注.传统的主元分析方法没有考虑故障数据中同时包含正常工况信息和故障信息,因而提取出故障子空间对故障的描述准确性不足.为提高故障子空间的准确性,提出一种基于广义主成分分析的重构故障子空间建模方法.首先,同时考虑正常工况数据和故障数据,...