A fuzzy-logic based fault diagnosis strategy for process control loops

By considering the fault propagation behaviors in process systems with control loops, a fuzzy-logic based fault diagnosis strategy has been developed in the present work. The proposed fault diagnosis methods can be implemented in two stages. In the off-line preparation stage, the fault origins of a system hazard are identified by determining the minimal cut sets of the corresponding fault tree. The fault propagation patterns in a feedback loop are obtained on the basis of system digraph. The occurrence order of observable symptoms caused by each fault origin is derived accordingly and then encoded into a set of IF-THEN diagnosis rules. In the next on-line diagnosis stage, the occurrence indices of the top event and also the fault origins are computed in a fuzzy inference system based on real-time measurement data. 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.     

Fault diagnosis with automata generated languages

A SDG-based simulation procedure is proposed in this study to qualitatively predict the effects of one or more fault propagating in a given process system. These predicted state evolution behaviors are characterized with an automaton model. By selecting a set of on-line sensors, the corresponding diagnoser can be constructed and the diagnosability of every fault origin can be determined accordingly by inspection. Furthermore, it is also possible to define a formal diagnostic language on the basis of this diagnoser. Every string (word) in the language is then encoded into an IF-THEN rule and, consequently, a comprehensive fuzzy inference system can be synthesized for on-line diagnosis. The language generation steps are illustrated with a series of simple examples in this paper. The feasibility and effectiveness of this approach has been tested in extensive numerical simulation studies.     

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     

基于符号有向图与定性趋势分析的化工过程故障诊断方法(英文)

In the past 30 years, signed directed graph (SDG), one of the qualitative simulation technologies, has been widely applied for chemical fault diagnosis. However, SDG based fault diagnosis, as any other qualitative method, has poor diagnostic resolution. In this paper, a new method that combines SDG with qualitative trend analysis (QTA) is presented to improve the resolution. In the method, a bidirectional inference algorithm based on assumption and verification is used to find all the possible fault causes and their corresponding consistent paths in the SDG model. Then an improved QTA algorithm is used to extract and analyze the trends of nodes on the consistent paths found in the previous step. New consistency rules based on qualitative trends are used to find the real causes from the candidate causes. The resolution can be improved. This method combines the completeness feature of SDG with the good diagnostic resolution feature of QTA. The implementation of SDG-QTA based fault diagnosis is done using the integrated SDG modeling, inference and post-processing software platform. Its application is illustrated on an atmospheric distillation tower unit of a simulation platform. The result shows its good applicability and efficiency.     

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.     

Controlled output variance based diagnosis tree for feedforward/cascade control systems

This paper aims at providing a framework for detection and diagnosis of the performance of a combinational feedforward (FF) and cascade (CC) control system. It is the extension of our previous work [1,2]. The main idea is to extract the only CC effect and the combination of FF with CC effects, respectively. In the only CC effect, the output variances of the primary and the secondary loops can be turned into the cascade-invariant and cascade-dependent terms, respectively. The combination of FF with CC effect can also be decomposed into the cascade/feedforward invariant term, the cascade-invariant/feedforward-dependent term and the cascade/feedforward dependent term. The diagnosis tree based on these decomposition terms is proposed to assess the performance of the FF/CC control system. The sequence of the statistical inference system is developed to diagnose fault causes. The capability of the proposed method is demonstrated via a cascade control system with the feedforward loops and multiple faults.     

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。     

基于部分信息融合的分布式故障诊断策略在分布式发电系统中的应用

将模型的故障监测框架和基于数据的学习方法相结合,提出了一种新的故障监测方法,实现了串级工业过程中的故障监测和定位。首先,对串级工业系统进行分析,得出整个系统的划分方法,并提供了构建子系统的条件。然后,采用分布式主元分析(PCA)方法在实际测量数据集中提取子系统的特征信息,用于TS模糊推理的建模。再提出基于二级贝叶斯的模糊模型实现故障的非线性识别。通过比较模型输出和每个子系统的实际测量值构造残差监测故障,实现定位。最后,通过固体氧化物燃料电池(SOFC)系统仿真实验,验证了所提出的故障监测模型的有效性和可行性。     

基于多阶段多向核熵成分分析的间歇过程故障检测方法

针对间歇过程的非线性、多阶段特性,提出一种基于多阶段多向核熵成分分析（multistage-MKECA,MsMKECA）的故障检测方法。针对间歇过程的多阶段特性,建立一种时序核熵主元关联度的矩阵相似性阶段划分方法,实现对间歇生产过程的多阶段划分;针对传统批次展开方式在线监控需要预估批次未来值的缺陷,进一步引入一种批次-变量三维数据展开方式建立每个阶段的MKECA非线性统计模型,实现对间歇过程的分阶段监控。最后对盘尼西林发酵过程开展仿真研究,结果表明所提方法能够比传统MKECA方法更为快速地进行故障检测。     

Three-way data analysis with time lagged window for on-line batch process monitoring

In this paper, on-line batch process monitoring is developed on the basis of the three-way data structure and the time-lagged window of process dynamic behavior. Two methods, DPARAFAC (dynamic parallel factor analysis) and DTri-PLS (dynamic trilinear partial least squares), are used here depending on the process variables only or on the process variables and quality indices, respectively. Although multivariate analysis using such PARAFAC (parallel factor analysis) and Tri-PLS (trilinear partial least squares) models has been reported elsewhere, they are not suited for practicing on-line batch monitoring owing to the constraints of their data structures. A simple modification of the data structure provides a framework wherein the moving window based model can be incorporated in the existing three-way data structure to enhance the detectability of the on-line batch monitoring. By a sequence of time window of each batch, the proposed methodology is geared toward giving meaningful results that can be easily connected to the current measurements without the extra computation for the estimation of unmeasured process variables. The proposed method is supported by using two sets of benchmark fault detection problems. Comparisons with the existing two-way and three-way multiway statistical process control methods are also included.     

基于建筑瓷砖烧成缺陷专家系统的模糊推理原型分析

针对建筑瓷砖烧成缺陷诊断问题及其特点,提出了一种基于诊断知识的模糊描述和模糊推理方法,阐述了建筑瓷砖烧成缺陷诊断专家系统中前向推理,后向推理及正反向混合推理模糊断言可信度的计算方法,并给出了相应的实例。     

基于循环自动编码器的间歇过程故障监测

针对间歇过程数据非线性、动态性特征,提出一种基于循环自动编码器(recurrent autoencoder,RAE)的过程故障监测方法。采用长短时记忆(long short-term memory,LSTM)循环神经网络构建自动编码器建立监控模型,相比传统自动编码器,其能有效挖掘时序样本间的动态关联信息。该方法首先利用批次展开与变量展开相结合的三步展开方法将间歇过程数据展开成二维,并通过滑动窗采样得到模型输入序列;然后使用LSTM构建自动编码器,重构输入序列。进一步,利用重构误差构造平方预测误差(squared prediction error, SPE)统计量实现在线监测。最后将所提方法应用于青霉素发酵仿真和重组大肠杆菌发酵过程监测,结果表明,该方法能及时监测到故障,具有较好的监测性能。     

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。     

基于一类赋时模糊Petri网的化工过程异常事件监控

化工生产中出现的异常事件往往导致系统出现故障,甚至发生重大事故,因此,建立预测和诊断异常事件的监控系统对生产过程的有效和稳定操作具有重要的意义。为此提出了一类赋时模糊Petri网（tFPN）模型,用于化工过程异常事件的预测与诊断,tFPN的变迁与产生式规则的可信度和时间相关联,可自动进行模糊推理。建立了基于tFPN的异常事件综合监控方案,并以聚丙烯反应为例进行了详细讨论。     

A fault magnitude based strategy for effective fault classification

A common approach in fault diagnosis is monitoring the deviations of measured variables from the values at normal operations to identify the root causes of faults. When the number of conceivable faults is larger than that of predictive variables, conventional approaches can yield ambiguous diagnosis results including multiple fault candidates. To address the issue, this work proposes a fault magnitude based strategy. Signed digraph is first used to identify qualitative relationships between process variables and faults. Empirical models for predicting process variables under assumed faults are then constructed with support vector regression (SVR). Fault magnitude data are projected onto principal components subspace, and the mapping from scores to fault magnitudes is learned via SVR. This model can estimate fault magnitudes and discriminate a true fault among multiple candidates when different fault magnitudes yield distinguishable responses in the monitored variables. The efficacy of the proposed approach is illustrated on an actuator benchmark problem.     

一种基于改进MPCA的间歇过程监控与故障诊断方法

针对基于不同展开方式的多向主元分析(MPCA)方法在线应用时各自存在的缺陷,提出一种改进的基于变量展开的MPCA方法,实现间歇过程的在线监控与故障诊断。该方法采用随时间更新的主元协方差代替固定的主元协方差进行T²统计量的计算,充分考虑了主元得分向量的动态特性;同时引入主元显著相关变量残差统计量,避免SPE统计量的保守性,且该统计量能提供更详细的过程变化信息,对正常工况改变或过程故障引起的T²监控图变化有一定的识别能力;最后提出一种随时间变化的贡献图计算方法用于在线故障诊断。该方法和MPCA方法的监控性能在一个青霉素发酵仿真系统上进行了比较。仿真结果表明：该方法具有较好的监控性能,能及时检测出过程存在的故障,且具有一定的故障识别和诊断能力。     

Hierarchical monitoring of industrial processes for fault detection,fault grade evaluation,and fault diagnosis

Traditional process monitoring methods cannot evaluate and grade the degree of harm that faults can cause to an industrial process. Consequently, a process could be shut down inadvertently when harmless faults occur. To overcome such problems, we propose a hierarchical process monitoring method for fault detection, fault grade evaluation, and fault diagnosis. First, we propose fault grade classification principles for subdividing faults into three grades: harmless, mild, and severe, according to the harm the fault can cause to the process. Second, two‐level indices are constructed for fault detection and evaluation, with the first‐level indices used to detect the occurrence of faults while the second‐level indices are used to determine the fault grade. Finally, to identify the root cause of the fault, we propose a new online fault diagnosis method based on the square deviation magnitude. The effectiveness and advantages of the proposed methods are illustrated with an industrial case study. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2781–2795, 2017     

基于光纤传感的石化机械振动监测系统开发

提出了应用光纤传感技术对石化设备进行振动监测的方法。开发了基于虚拟仪器Lab-VIEW的监测系统,分析了故障判断原理。该系统既实现监测数据的远程传输与在线分析功能,又具有完善的离线分析与显示功能。利用该系统对某型号往复式压缩机进行了现场测试,结果表明该系统具备了对石化设备进行实时监测的能力。     

基于收敛交叉映射的化工过程扰动因果分析方法

当化工装置出现异常情况,操作工人往往无法及时准确地定位故障发生的原因。基于数据的方法能够通过化工过程数据,分析异常工况中的扰动传播路径,确定异常工况出现的根本原因。针对化工动态系统,提出了具有时间特性的收敛交叉映射方法（CCM）,和基于赤池信息准则的维度选择方法。为了验证提出算法的有效性,在简单的生态系统,因果检测基准系统和全混釜反应器（CSTR）中进行验证,并与原有的收敛交叉映射算法进行对比,体现出具有时间特性的收敛交叉映射算法的优越性。