Fuzzy diagnosis method for control systems with coupled feed forward and feedback loops

By considering the fault propagation behaviors in control systems with coupled feed forward and feedback loops, a fuzzy-logic-based fault diagnosis strategy has been developed in the present work. The proposed methods can be implemented in two stages. In the off-line preparation stage, the root causes of a system hazard are identified by determining the minimal cut sets of the corresponding fault tree. The occurrence order of observable disturbances caused by each fault origin is derived from the system digraph. All possible patterns of the on-line symptoms and their evolution sequences can then be deduced accordingly. These sequences are used as the basis for constructing a two-layer fuzzy inference system. In the next on-line implementation stage, the occurrence indices of the root causes are computed with the IF-THEN rules embedded in the inference engine using the real-time measurement data. Numerical simulation studies have been carried out to demonstrate the feasibility of the proposed approach.     

A fuzzy diagnosis approach using dynamic fault trees

By incorporating digraph models, fault trees and fuzzy inference mechanisms in a unified framework, a novel approach for fault diagnosis is developed in this work. To relieve the on-line computation load, the fault origins considered in diagnosis are limited to the basic events in the cut sets of a given fault tree. The symptom occurrence order associated with each root cause is derived from system digraph with the qualitative simulation techniques. The implied candidate patterns are enumerated according to two proposed theorems and then encoded in the inference system with IF-THEN rules. The simulation results show that the proposed approach is not only feasible but also capable of identifying the most likely cause(s) of a hazardous event at the earliest possible time.     

面向系统层级的复杂工业过程全息故障诊断

复杂工业过程具有长流程、系统层级多、故障潜在分布空间范围较广的特点，是当前故障诊断领域的热门研究方向。首先，对主流故障诊断技术进行了分类和概述；其次，采用定量与定性相结合思路，提出了面向系统层级的复杂工业过程全息故障诊断框架，为复杂工业全流程的过程监测提供一整套技术和解决方案。相比于目前的故障诊断方法，该框架不仅包括故障检测和故障辨识，还包括故障根源诊断、故障传播路径识别、故障的定量诊断与评估，可有效解决复杂工业过程系统的综合故障诊断问题，实用性强，能够有效地减少或避免故障发生、保证产品的质量、提高企业的生产效率与生产安全；最后对故障诊断技术的发展趋势和亟待解决的问题进行了展望。     

Petri-net models for comprehensive hazard analysis of MOCVD processes

Reducing contamination level is of primary importance for the safety and efficiency of a MOCVD process. Off-line fault identification is one of the basic tasks that must be performed in hazard analysis to identify potential operational problems. For illustration convenience, the scope of present study is limited to the purge-gas purifier of the process. A systematic step-by-step procedure is proposed in this paper to construct Petri nets for modeling the purification system. Efficient hazard assessment studies have been performed by simulating the fault propagation behaviors on the basis of the system model. A comprehensive list of possible fault origins has been thoroughly examined to demonstrate the effectiveness of the proposed approach.     

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     

炼化装置故障链式效应定量安全预警方法

炼化装置故障及其故障链式效应对油气生产和人民生命安全所造成的危害严重。从故障链角度进行事故风险研究,提出炼化装置故障链式效应定量安全预警方法。首先分析炼化装置故障链式关系结构,基于目标树成功树-动态主逻辑图（GTST-DMLD）建立其故障链式效应关系模型,揭示炼化装置故障链式效应行为规律和关联本质,从而评价装置异常工况下的安全状态。进一步以马尔可夫过程为理论基础,建立故障链式效应预测模型,预测故障传播的后果和方向,并计算各后果的发生概率,为现场操作人员进行主动维修或应急处置提供依据。案例分析中通过对某化工厂常压塔装置、减压炉装置为研究对象进行应用与验证,结果表明该方法可以准确地对系统故障发生后的状态进行评价和预测,方法有效、可行,便于操作人员在处置已有故障的同时,注意预防其他异常工况的发生,降低油气生产加工过程中的整体风险。     

FAULT DETECTION AND DIAGNOSIS IN BATCH AND SEMI-BATCH PROCESSES USING ARTIFICIAL NEURAL NETWORKS

The objective of this work is to assess the feasibility of adopting artificial neural networks (ANNs) in fault detection and diagnosis for batch and semi-batch processes. Although there is a large volume of related publications available, most of them used steady-state data to train ANNs and, as such, the task of fault diagnosis can only be implemented in continuous operations. Based upon the concept of analytical redundancy, the framework of a two-stage fault monitoring system is proposed in this paper. In the first stage, a hybrid ANN is adopted to predict the long-term dynamic behaviors of the output variables under normal condition. The occurrence of fault(s) can be detected by inspecting the residuals, i.e. the differences between the measured and the predicted values of outputs. A second feedforward neural network is then used for the purpose of differentiating the residual patterns caused by various faults. In addition to the fact the results of pilot tests are quite satisfactory, it is also demonstrated in our experimental studies that the proposed fault-monitoring system is capable of detecting and diagnosing faults that cannot be described by traditional mathematical models.     

Using improved self-organizing map for fault diagnosis in chemical industry process

There are numerous fault diagnosis methods studied for complex chemical process, in which the effective methods for visualization of fault diagnosis are more challenging. In order to visualize the occurrence of the fault clearly, a novel fault diagnosis method which combines self-organizing map (SOM) with correlative component analysis (CCA) is proposed. Based on the sample data, CCA can extract fault classification information as much as possible, and then based on the identified correlative components, SOM can distinguish the various types of states on the output map. Further, the output map can be employed to monitor abnormal states by visualization method of SOM. A case study of the Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The results show that the SOM integrated with CCA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.     

基于改进差别矩阵属性约简的聚合釜粗糙集-神经网络故障诊断

引言 聚氯乙烯树脂(PVC)是重要的有机合成材料,其产品具有良好的物理性能和化学性能,广泛应用于工业、建筑、农业、电力、公用事业等领域.聚合釜则是聚氯乙烯生产装置的关键设备,聚合釜能否稳定运行直接关系到整个聚氯乙烯生产装置的运行状况.     

A hybrid support vector machine and fuzzy reasoning based fault diagnosis and rescue system for stable glutamate fermentation

In industrial glutamate fermentation by biotin-auxotroph Corynebacterium glutamicum, biotin content variation in corn slurry greatly affects fermentation performance. To maintain the fermentation stability, a hybrid support vector machine (SVM) and fuzzy reasoning based fault diagnosis and rescue system was developed. The system uses SVM outputs as the inputs of the fuzzy reasoning classifier having a couple of production rules and condition membership functions related with SVM outputs, to categorize multiple faults. The effectiveness of the proposed system was verified in a normal fermentation run and two abnormal runs with different biotin initial-content faults with the aid of using on-line measurable data such as ammonia consumption rate and CO₂ evolution rate. The results indicated that the proposed faults-diagnosis system could cluster multiple fermentation faults quickly, accurately and stably, and faults and their types could be identified at the earliest fermentation stage. Based on the diagnosis results, the proposed system was further applied for real fault-rescue in two fermentations with different biotin initial-content faults. In both cases, by immediately taking relevant rescue measures after identifying the faults and their types, glutamate fermentations with initial faults were restored to normal, and final glutamate concentrations reached a normal level of 75–80 g/L at 34 h.     

基于决策分类方法的水泥回转窑故障诊断方法

提出了一种基于决策分类的水泥回转窑故障诊断方法。该方法利用水泥生产过程中产生的回转窑历史数据结合模糊决策树算法生成故障诊断模糊推理规则库,利用在线数据可对模糊规则库进行增量学习,不断完善故障诊断规则库。故障诊断阶段,将在线数据形成的特征向量与故障诊断规则库进行匹配,判断是否发生故障以及相应的故障类型。该方法对故障数据进行模糊化处理,提高了故障诊断结果的可解释性以及泛化性能,适用于实际的水泥生产过程。     

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.     

间歇过程的故障分析和互锁控制器综合的集成

Integration amongst various decision-making processes, such as planning, design, and operation is necessary to dynamic and flexible batch production. To achieve a batch production integration, utilization of common models used for various decision-making processes is an effective approach. From this point of view, a batch system common model as described by a Petri net is proposed. In this article, a fault diagnosis technique for batch processes is presented using information about fault propagation and the possibilities of integration of fault analysis and controller synthesis are discussed on the basis of the Petri net based common models.     

远程故障诊断及其在气垫式流浆箱控制中的应用

在简要介绍故障诊断的基本原理和主要方法之后,给出了远程故障诊断的概念,以一五叠网气垫式流浆箱控制系统为例,设计了一种低成本的基于西门子S7-300 PLC的远程故障诊断系统,实际应用效果证明了该系统的有效性.     

Fault detection of large-scale process control system with higher-order statistical and interpretative structural model

Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical (HOS) is an effec-tive data-driven method, but the calculation costs much for a large-scale process contr...     

基于高阶统计和解释结构模型相结合的大型过程控制系统的故障检测(英文)

Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical (HOS) is an effec-tive data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model (ISM) and HOS is proposed:(1) the adja-cency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method;and (4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamical y based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.     

Integrated nonlinear model predictive fault tolerant control and multiple model based fault detection and diagnosis

In this paper, a new fault-tolerant control approach is presented for a class of nonlinear systems, which preserves system stability despite a time delay in fault detection. The faults are assumed to occur in the actuators and are modeled for the general form of affine nonlinear systems. A fault detection and diagnosis (FDD) block is designed based on the multiple model method. The bank of extended Kalman filters (EKF) is used to detect predefined actuator faults and to estimate the unknown parameters of actuator position. The estimated parameters are then used to correct the model of the faulty system and to reconfigure the controller. The reconfigurable controller is designed based on the stabilizing nonlinear model predictive control (NMPC) scheme. On the other hand, in the duration between fault occurrence and fault detection, because of the mismatch between the process and the model, the system states may go off the attraction region. The proposed method is based on designing multiple local controllers for individual predefined faults. Depending on the value of a system variable at the moment of fault detection, one of these controllers will operate. This leads to a stability region of a set of auxiliary equilibrium points (AEPs), which is larger than the attraction region. Moreover, a framework for preserving system stability is presented. Finally, a practical chemical process example is presented to illustrate the effectiveness of this method.     

Fault diagnosis based on imbalance modified kernel Fisher discriminant analysis

Process data with imbalance class distribution has brought a significant drawback to most existing pattern recognition based fault diagnosis algorithms, which have assumed that the process data have an equal misclassification cost and relatively balanced class distribution. The frequent occurrence of the imbalance problem in real industrial process indicates the need for extra research efforts. In this paper, three novel imbalance modified kernel Fisher discriminant analysis (IM-KFDA) approaches are proposed to handle this problem. Two sample-level approaches, over-sampling KFDA and under-sampling KFDA, are presented along with proper stochastic sampling strategies. One algorithm-level approach, inductive bias KFDA, is also proposed with incorporating a novel regular weighted matrix (RWM) into the minimum Euclid distance based pattern classification rule. To improve the fault diagnosis performance, model updating modes for the sample-level and algorithm-level approaches are described, respectively. A simulation case study of Tennessee Eastman (TE) process is conducted to evaluate the proposed fault diagnosis approaches.     

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。     

基于非线性动态模型的质子交换膜燃料电池故障诊断

为了对质子交换膜燃料电池(proton exchange membrane fuel cell,PEMFC)系统进行故障诊断以提高系统的安全性和可靠性,针对PEMFC系统的强非线性,在九阶状态空间模型的基础上提出一种滑模观测器实时生成残差,利用故障阈值检测法建立故障特征矩阵检测故障,进而为了隔离故障,引入相对故障敏感度函数建立理论相对故障敏感度矩阵,在系统运行时实时计算各故障相对故障敏感度与理论相对故障敏感度的欧氏距离,最小欧氏距离对应的故障则为系统发生的故障,结果验证了所提出的基于模型的故障诊断方法的有效性,且所构建观测器可以估计PEMFC系统中难以直接测取的状态变量,平均相对误差在6%以内。