基于模糊分层SDG模型的故障推理方法

针对传统 SDG 模型诊断方法存在的诊断分辨率低、速度慢、效率低等不足,提出了一种基于模糊分层SDG模型的故障推理方法. 首先建立系统的SDG模型,并进行分层;再利用模糊变量表示节点变量,用条件概率表表达节点间的定性因果关系;最后利用贝叶斯推理和回溯搜索找出故障源候选集,并对候选解进行排序. 应用提出的方法,进行了某型号航空发动机燃油调节系统故障诊断,结果表明该方法能够提高诊断的分辨率和效率,诊断结果准确而且完备.     

SDG建模及其应用的进展

符号有向图SDG(signed directed graph)是描述大规模复杂系统的一种有效方式,通过节点和有向支路表示了系统变量或局部之间的因果影响关系.近年来,关于SDG的研究已经成为热点并已取得许多成果,特别是在安全分析领域得到了重要的应用.本文在概述了SDG方法的产生背景与发展近况的基础上,主要综述了SDG研究中的若干重要问题,包括SDG模型的数学描述和基于数学模型、流程图和经验知识的三种建模方法,以及SDG在安全评价和故障诊断领域研究和应用的相关成果,总结了相关方法的优缺点,其中的核心问题是推理方法及其效率.最后对SDG技术的发展方向做出了展望,定量信息引入、推理方法、计算机建模等方面都有待于进一步研究.     

模糊SDG故障诊断方法及其应用

基于SDG的故障诊断方法在使用过程中,由于阈值设定不合理会导致故障的误报或漏报。针对该问题展开研究,提出模糊SDG模型,建立五级SDG模型并引入参数模糊隶属度,提出相应的模糊推理算法。通过将模糊SDG模型及其推理方法应用于某常减压蒸馏装置进行故障诊断实例分析,验证了方法的有效性和可行性。     

大型复杂系统的动态SDG模型及传感器布置问题

符号有向图(SDG: signed directed graph)可以用于描述大型复杂系统及其变量之间的因果影响关系, 但是在描述故障传递关系时不能表示其动态传播规律. 本文在SDG支路上引入时间参数, 用于近似描述变量的变化在系统中的传递时间, 由此构建的SDG模型称为动态SDG模型. 另外, 要实现故障检测需要有传感器的信息, 传感器的布置直接影响着故障检测的性能. 本文在动态SDG框架下, 研究了故障的可检测性和可分辨性问题, 提出了一些一般性的结论, 并给出通过正向推理来求故障传播过程和传感器布置方案的方法. 最后, 通过实例验证该方法的有效性.     

基于FPSDG模型的故障检测传感器的分布优化设计

针对航天器的故障检测问题,本文提出了模糊概率符号有向图(SDG)系统模型,并与传统SDG模型进行了比较说明;讨论了监控变量的初选取原则,提出了传感器分布优化设计方案,该方案既考虑了故障的可观测性,又考虑了故障传播权重和传感器监控成本的约束问题,方案采用了贪婪启发式算法,通过计算机程序实现了该算法,最后建立了某卫星一次电源系统的诊断模型,应用以上提出的方法,进行了故障检测传感器的分布仿真,结果证明了该方法的有效性。     

SDG故障诊断中的分层建模递阶推理方法

针对采用符号有向图(signed directed graph,SDG)对石化工业系统进行故障诊断时,经常存在SDG建模和推理过程十分繁琐、困难的问题,提出了一种基于控制回路分解系统的SDG分层建模及递阶推理方法.在建立SDG模型时将整个系统分解为含有系统层、子系统层和回路层的多层次模型架构,进而利用递阶推理在分层SDG模型中搜索相容通路来实现故障诊断.该方法能清晰表述工业系统的SDG模型,减少了建模和推理的复杂度.以Tennessee Eastman仿真系统为例进行了验证,证明了方法的有效性.     

基于模糊推理的SDG故障诊断新方法

针对纯定性的SDG推理方法忽略了SDG图中节点之间的影响程度不同导致诊断分辨率不高这一问题,提出了在纯定性SDG推理的基础上用模糊矩阵的形式加入节点间相互影响关系的定量信息的推理新方法,可对多潜在故障源划分优先级,从而提高SDG故障诊断的分辨率。相对于其他模糊SDG故障诊断方法,本方法勿需使用隶属函数,取而代之的是模糊矩阵,后者的获取易于前者,且采用矩阵的表示方法方便了计算机编程的实现。     

综合性SDG故障诊断架构

基于模型的SDG(Signed DiGraph,符号有向图)故障诊断方法因其具有完备性好、推理深度高等优点在过程工业安全工程中具有十分重要的意义,已成为安全工程中的1种关键技术。本文在以前研究的基础上,提出了1个综合性SDG故障诊断架构,以期能够实现在生产过程中及时发现故障并判明故障源。该综合性故障诊断架构按模型、推理和应用3个层次搭建,以传统定性SDG及概率SDG理论为基础,包含了从模型建立到故障诊断推理,从定性SDG方法到结合统计监控的SDG方法再到概率SDG方法等一系列实施方案。该综合性SDG故障诊断架构由于引入了多元统计监控模型,使得在系统没有表现出明显的故障征兆时就能够及时敏感地检测到异常变化,进而触发SDG及PSDG推理来实现对故障源的查找,并给出各故障源发生故障的概率值,以指导使用者按照概率值的大小顺序采取处理措施。以某石化公司的气体分馏装置为实际背景,利用该装置实时数据库中的实际生产工艺数据对该综合性诊断架构进行了实例验证,其故障诊断结果与实际发生的故障相吻合,证明了该综合性故障诊断架构的有效性。     

SDG故障诊断规则的知识约简

符号有向图(SDG)是揭示流程系统深层知识的定性模型,用于描述流程系统的状态变量及其变量间的故障信息传递关系.当系统的状态变量过多,运用SDG故障诊断算法生成的故障规则过于庞大,推理困难.粒矩阵的知识约简算法能有效约简冗余属性.因此,将粒矩阵的知识约简算法引入SDG故障诊断,以电站除氧器系统为例,使用粒矩阵的知识约简算法约简主要故障的故障规则,简化规则中的冗余节点,提高故障诊断效率,最后验证了约简后的故障诊断规则的正确和有效.     

基于符号模型检测的符号有向图故障诊断解形式化验证

由于定量信息和非线性因果关系的丢失,符号有向图(SDG)的故障诊断解需要进一步进行校核与验证.将SDG故障诊断解的验证置于符号模型检测框架中进行研究,提出了基于符号模型检测的SDG故障诊断解形式化验证方法.首先定义了SDG模型的有限状态变迁系统形式化描述,建立了符号模型检测(SMV)模型;其次引入故障传播时间定义了模型观测变量的动态验证信息,提出了基于步进式监控的动态推理验证策略;然后扩展了动态推理验证过程的SMV模型,提出了验证算法SSDGFD_SMC;最后,通过一个简单贮水罐系统的SDG模型实例验证了算法SSDGFD_SMC的有效性.     

基于符号模型检测的SDG故障诊断解形式化验证

由于定量信息和非线性因果关系的丢失,SDG的故障诊断解需要进一步的进行校核与验证。创新地将SDG故障诊断解的验证置于符号模型检测框架中进行研究,提出了基于符号模型检测的SDG故障诊断形式化验证方法。首先扩展、转换了SDG模型的有限状态变迁系统形式化描述,建立了SMV模型;其次引入故障传播时间建立了模型观测变量的动态验证信息,并基于步进式监控分析了动态验证策略,将SDG正向推理扩展建模为动态推理验证;然后面向符号模型检测扩展了动态推理验证过程的SMV模型,提出了验证算法SSDGFD_ SMC;最后,通过一个实例验证了算法的有效性。     

模糊概率SDG模型及故障推理方法

基于符号有向图(SDG)的故障诊断方法具有良好的完备性和易于解释性,但其存在分辨率差的缺陷,为此提出基于模糊概率SDG模型和贝叶斯推理的半定量故障诊断方法.用模糊变量表示节点变量,用条件概率表(CPT)表达节点间的定性因果关系,利用贝叶斯推理和回溯搜索找出故障源候选解的集合,并对候选解进行排序.最后建立了某卫星一次电源系统的诊断模型.仿真结果表明,该方法有效地提高了诊断的分辨率,适用于航天器在轨故障诊断.     

Model-based kernel sum rule: kernel Bayesian inference with probabilistic models

Machine Learning - Kernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned...     

Ising图模型概率推理的计算复杂性

图模型概率推理的主要任务是通过对联合概率分布进行变量求和来计算配分函数、变量边缘概率分布、条件 概率分布等。图模型概率推理计算复杂性及近似概率推理的计算复杂性是一重要的理论问题,也是设计概率推理算 法和近似概率推理算法的理论基础。研究了Ising图模型概率推理的计算复杂性,包括概率推理的难解性及不可近似 性。具体地,通过构建#2 SA"I'问题到Icing图模型概率推理问题的多项式时间计数归约,证明在一般 Ising图模型上 计算配分函数、变量边缘概率分布、条件概率分布的概率推理问题是#P难的,同时证明Icing图模型近似概率推理问 题是NP难的,即一般Icing图模型上的概率推理问题是难解且不可近似的。     

On probabilistic inference by weighted model counting

A recent and effective approach to probabilistic inference calls for reducing the problem to one of weighted model counting (WMC) on a propositional knowledge base. Specifically, the approach calls for encoding the probabilistic model, typically a Bayesian network, as a propositional knowledge base in conjunctive normal form (CNF) with weights associated to each model according to the network parameters. Given this CNF, computing the probability of some evidence becomes a matter of summing the weights of all CNF models consistent with the evidence. A number of variations on this approach have appeared in the literature recently, that vary across three orthogonal dimensions. The first dimension concerns the specific encoding used to convert a Bayesian network into a CNF. The second dimensions relates to whether weighted model counting is performed using a search algorithm on the CNF, or by compiling the CNF into a structure that renders WMC a polytime operation in the size of the compiled structure. The third dimension deals with the specific properties of network parameters (local structure) which are captured in the CNF encoding. In this paper, we discuss recent work in this area across the above three dimensions, and demonstrate empirically its practical importance in significantly expanding the reach of exact probabilistic inference. We restrict our discussion to exact inference and model counting, even though other proposals have been extended for approximate inference and approximate model counting.     

Quantitative Inference by Qualitative Semantic Knowledge Mining with Bayesian Model Averaging

In this paper, we consider the problem of performing quantitative Bayesian inference and model averaging based on a set of qualitative statements about relationships. Statements are transformed into parameter constraints which are imposed onto a set of Bayesian networks. Recurrent relationship structures are resolved by unfolding in time to Dynamic Bayesian networks. The approach enables probabilistic inference by model averaging, i.e. it allows to predict probabilistic quantities from a set of qualitative constraints without probability assignment on the model parameters. Model averaging is performed by Monte Carlo integration techniques. The method is applied to a problem in a molecular medical context: We show how the rate of breast cancer metastasis formation can be predicted based solely on a set of qualitative biological statements about the involvement of proteins in metastatic processes.     

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We use a Gaussian process with hyper-parameters estimated from numerical weather prediction models, which yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.     

PrDB: managing and exploiting rich correlations in probabilistic databases

Due to numerous applications producing noisy data, e.g., sensor data, experimental data, data from uncurated sources, information extraction, etc., there has been a surge of interest in the development of probabilistic databases. Most probabilistic database models proposed to date, however, fail to meet the challenges of real-world applications on two counts: (1) they often restrict the kinds of uncertainty that the user can represent; and (2) the query processing algorithms often cannot scale up to the needs of the application. In this work, we define a probabilistic database model, PrDB, that uses graphical models, a state-of-the-art probabilistic modeling technique developed within the statistics and machine learning community, to model uncertain data. We show how this results in a rich, complex yet compact probabilistic database model, which can capture the commonly occurring uncertainty models (tuple uncertainty, attribute uncertainty), more complex models (correlated tuples and attributes) and allows compact representation (shared and schema-level correlations). In addition, we show how query evaluation in PrDB translates into inference in an appropriately augmented graphical model. This allows us to easily use any of a myriad of exact and approximate inference algorithms developed within the graphical modeling community. While probabilistic inference provides a generic approach to solving queries, we show how the use of shared correlations, together with a novel inference algorithm that we developed based on bisimulation, can speed query processing significantly. We present a comprehensive experimental evaluation of the proposed techniques and show that even with a few shared correlations, significant speedups are possible.