基于置信规则库专家系统的司控器开关量健康状态评估

微动开关是轨道车辆司控器常用的开关设备,对其健康状态评估是保证轨道车辆运行安全的前提.针对司控器微动开关数据样本少、诊断信号具有波动性和非线性、健康状态评估困难等问题,提出一种基于置信规则库专家系统(BRB)的司控器开关量健康状态评估方法.首先,分析微动开关失效机理与故障特征的关系;然后,采用置信规则库将定性知识与定量信息有效结合,采用证据推理(ER)算法进行知识推理,并对所建立的模型初始参数进行优化,得到最优的参数集合,从而提高轨道车辆微动开关健康状态评估的准确性.通过对模型训练及测试,所得结果表明,所提出的方法能准确地评估微动开关状态,便于早期发现故障、跟踪故障发展趋势和及时更换失效部件.     

基于置信规则库的工业控制网络入侵检测

针对工业控制网络易遭受恶意攻击,本文提出了一种基于置信规则库的工业控制网络入侵检测方法。当置信规则库的前提属性数目过多时,置信规则库的规则条数呈指数级别增长,容易导致"组合爆炸"问题,本文提出利用线性组合方式构建置信规则库中的规则。本文还利用证据推理算法对置信规则库中的置信规则进行组合,并且优化置信规则库初始参数,提高了入侵检测的精确度。     

基于置信规则库和弹道仿真的加速度计健康状态评估

结合加速度计运行机理复杂,测试样本少的特点,提出一种基于置信规则库(BRB)和弹道仿真的健康状态评估模型.首先,基于BRB建立初始的健康状态评估模型,由于定性知识的主观性,初始的评估模型难以提供准确的评估结果而需要被优化,然而,在以往的研究中优化所需的健康状态真实值一般通过专家给定,存在模糊性和不精确的问题,因此,根据加速度计在导航过程中的物理模型,基于弹道仿真方法准确地计算其健康状态的真实值;然后,基于协方差自适应进化算法(P-CMA-ES),健康状态真实值被用于初始评估模型的优化,从而准确、快速地进行健康状态评估;最后,以某型加速度计的健康状态评估为例,验证所提出方法的有效性.     

基于证据推理和置信规则库的装备寿命评估

针对航天产品试验样本少,寿命评估难的特点,结合产品在研制阶段多种工作环境的失效数据,提出了一种基于证据推理(evidential reasoning,ER)和置信规则库(belief-rule-base,BRB)进行装备寿命评估的新方法.首先,分析了模型的合理性并使用多维BRB模型将多种环境下的寿命数据折合为标准工作环境下的寿命数据,然后通过ER算法将折合后数据和实际工作环境数据进行融合.其次,详细说明了BRB--ER模型的推理过程和寿命评估的步骤.最后,采用某航天产品的失效数据对该方法进行了验证,并用已有的产品寿命的固定值进行BRB的参数更新.研究结果表明,在专家知识准确合理时,该模型能够准确地评估产品寿命,并可根据已有的产品的固定寿命进行训练,建立更加准确的寿命预测模型.     

基于置信规则库专家系统的发动机故障诊断

针对发动机故障原因和征兆之间存在的复杂非线性关系,利用RIMER(基于证据推理算法的置信规则库推理方法)对发动机进行故障诊断,克服了传统专家系统或神经网络技术只能单一利用专家知识或训练数据的缺点,将定性知识与定量数据有效结合,对发动机故障原因进行了研究,给维修人员提供了重要参考依据,仿真实验结果表明该方法可行有效.     

基于改进置信规则库推理的分类方法

通过引入置信规则库的线性组合方式,设定规则数等于分类数及改进个体匹配度的计算方法,提出了基于置信规则库推理的分类方法。比较传统的置信规则库推理方法,新方法中规则数的设置不依赖于问题的前件属性数量或候选值数量,仅与问题的分类数有关,保证了方法对于复杂问题的适用性。实验中,通过差分进化算法对置信规则库的规则权重、前件属性权重、属性候选值和评价等级的置信度进行参数学习,得到最优的参数组合。对3个常用的公共分类数据集进行测试,均获得理想的分类准确率,表明新分类方法合理有效。     

基于BK树的扩展置信规则库结构优化框架

针对扩展置信规则库(extended belief rule base,EBRB)系统在规则数较多时推理效率不理想的问题,引入BK树数据结构,提出了一种基于BK树的结构优化框架。首先根据置信规则在度量空间中彼此的距离建立EBRB的树形索引结构,然后通过设置阈值减少EBRB系统推理时搜索规则的数量,并激活关键规则,最终达到提高EBRB系统推理效率的目的。以非线性函数拟合、输油管道泄露仿真实验及分类数据集的对比实验,验证结构优化框架在EBRB系统中的有效性,实验结果表明,所提框架能够优化EBRB系统推理效率并提高决策准确性。     

一种基于改进分层置信规则库的社交账户可信度评估方法

社交账户可信度评估是确保网络社交生态良性发展的重要环节。针对社交账户可信度评估指标多维、数据信息不确定性多样等问题,提出了一种基于改进分层置信规则库的可信度评估方法。首先从账户属性、交际属性和内容属性三个角度分析了可信度评估各指标之间的相互关系,并依此构建了置信规则库的分层结构。其次,在信息转换函数中引入了自适应系数以更好描述和处理指标间的特性差异。最后,为了弥补专家知识局限性带来的模型误差,采用带有投影算子的协方差矩阵自适应进化策略对自适应系数和模型参数进行了优化。以新浪微博账户作为实验对象,结果表明该方法能够在数据样本有限的情况下获得更高的可信度评估精度。     

基于置信规则库的应急方案调整方法

针对运用案例推理生成应急方案的问题,提出了一种基于置信规则库的方案调整方法．依据案例检索方法得到与目标案例最相似的历史案例,计算案例库中其他案例与最相似历史案例的问题差值,并转换为评价等级置信度形式;再利用历史案例信息及学习模型确定案例规则库的参数．在此基础上,将目标案例与最相似案例的问题差值转换为评价等级置信度形式,通过置信规则库的推理规则得到目标案例的方案．最后,通过一个环境突发事件的救援算例来说明该方法的可行性和有效性．     

基于置信规则库的飞控系统故障诊断

针对传统飞控系统故障诊断中存在的因引入专家知识引起的主观偏差问题和使用数据驱动方法因数据量不足导致的过拟合问题,提出了基于置信规则库推理的飞控系统故障诊断。根据已有故障知识构建飞控系统故障诊断置信规则库,利用测试过程中获得的故障数据,以数值样本优化学习模型对置信规则库参数进行训练。实例表明,经少量样本训练后的置信规则库可以很好地解决初始置信规则库参数存在主观偏差的问题,经实验证明该方法能够实现高效可靠的飞控系统故障诊断。     

New model for system behavior prediction based on belief rule based systems

To predict the behavior of a complex engineering system, a model can be built and trained using historical data. However, it may be difficult to obtain a complete and accurate set of data to train the model. Consequently, the model may be incapable of predicting the future behavior of the system with reasonable accuracy. On the other hand, expert knowledge of a qualitative nature and partial historical information about system behavior may be available which can be converted into a belief rule base (BRB). Based on the unique features of BRB, this paper is devoted to overcoming the above mentioned difficulty by developing a forecasting model composed of two BRBs and two recursive learning algorithms, which operate together in an integrated manner. An initially constructed forecasting model has some unknown parameters which may be manually tuned and then trained or updated using the learning algorithms once data become available. Based on expert intervention which can reflect system operation patterns, two algorithms are developed on the basis of the evidential reasoning (ER) algorithm and the recursive expectation maximization (EM) algorithm with the former used for handling judgmental outputs and the latter for processing numerical outputs, respectively. Using the proposed algorithms, the training of the forecasting model can be started as soon as there are some data available, without having to wait until a complete set of data are all collected, which is critical when the forecasting model needs to be updated in real-time within a given time limit. A numerical simulation study shows that under expert intervention, the forecasting model is flexible, can be automatically tuned to predict the behavior of a complicated system, and may be applied widely in engineering. It is demonstrated that if certain conditions are met, the proposed recursive algorithms can converge to a local optimum. A case study is also conducted to show the wide potential applications of the forecasting model.     

稀疏规则库条件下的模糊推理方法

当模糊规则库是稀疏型时,利用Kóczy线性插值推理方法不能保证推理结论的正规性和凸性,为了解决这一问题,石岩曾提出了插值推理方法的推理条件,当满足这些条件时利用Kóczy线性插值推理方法得到的推理结论也满足正规性和凸性;但是这些条件却限制了模糊推理系统的应用,而且如果多次推理中在同一输入点遇到稀疏情况,必须进行相同的计算才能得到正确的推理结果,这样增加了系统的计算量,降低了系统的速度和效率.因此提出了一种新的稀疏模糊推理方法,不仅能够简单的给出正确的推理结果,还能在相应的位置增加规则,提高规则库的紧密程度.     

Online updating belief rule based system for pipeline leak detection under expert intervention

A belief rule base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule base (BRB) is proposed to extend traditional IF-THEN rules and can capture more complicated causal relationships using different types of information with uncertainties, but these models are trained off-line and it is very expensive to train and re-train them. As such, recursive algorithms have been developed to update the BRB systems online and their calculation speed is very high, which is very important, particularly for the systems that have a high level of real-time requirement. The optimization models and recursive algorithms have been used for pipeline leak detection. However, because the proposed algorithms are both locally optimal and there may exist some noise in the real engineering systems, the trained or updated BRB may violate some certain running patterns that the pipeline leak should follow. These patterns can be determined by human experts according to some basic physical principles and the historical information. Therefore, this paper describes under expert intervention, how the recursive algorithm update the BRB system so that the updated BRB cannot only be used for pipeline leak detection but also satisfy the given patterns. Pipeline operations under different conditions are modeled by a BRB using expert knowledge, which is then updated and fine tuned using the proposed recursive algorithm and pipeline operating data, and validated by testing data. All training and testing data are collected from a real pipeline. The study demonstrates that under expert intervention, the BRB expert system is flexible, can be automatically tuned to represent complicated expert systems, and may be applied widely in engineering. It is also demonstrated that compared with other methods such as fuzzy neural networks (FNNs), the RIMER has a special characteristic of allowing direct intervention of human experts in deciding the internal structure and the parameters of a BRB expert system.     

基于模糊理论的造纸专家系统--知识库的设计

基于模糊理论的造纸专家系统(PMES)的研究对于保障设备的稳定运行,提高产品质量具有重要的意义.本文探讨了模糊理论在知识表示方面如何和专家系统进行有效地结合,并对PMES的知识库进行设计.对知识库采用知识分级的方式,使知识的表达更具有层次性.通过各个知识表中的相互约束关系保证知识的一致性.     

A data base management system for industrial process control

Present-day systems for monitoring energy distribution must be capable of handling continuous changes in the structure and the extent of the data. Only a data base system which satisfies the special requirements of process control can cope with this. The process real-time information management System for on-line Control (PRIMO) data base system described here is implemented on DEC PDP-11 computers from $\text{11}\text{34}$ upwards and is used for process control in Brown Boveri energy control software systems.     

基于数据挖掘的工业控制系统防危机制研究

随着信息技术和工业自动化水平的提升,工业控制系统越来越复杂,错误也越来越难检测和避免,且常常引发工业事故,危及工业生产、国家经济安全和人民生命财产安全。针对工控系统存在的安全问题,采用复杂网络分析、数据预测、专家分析、自适应升降级等技术,提出一种包含全局防危、主动防危、实时防危、自主防危和防危认证的工业控制系统防危机制,实现对工业控制系统的安全防护。     

Condition-based maintenance of dynamic systems using online failure prognosis and belief rule base

Condition-based maintenance has attracted an increasing attention both academically and practically. If the required physical models to describe the dynamic systems are unknown and the monitored information only reflects part of the state of the dynamic systems, expert knowledge is a source of valuable information to be used. However, expert knowledge is usually in a qualitative form, and therefore, needs to be transformed and combined with the measured characteristic information to provide effective prognosis. As such, this paper focuses on developing a novel approach to deal with the problem. In the proposed approach, a belief rule base (BRB) for the failure prognostic model is constructed using the expert knowledge and the analysis of the failure mechanism. An online failure prognostic algorithm is then proposed on the basis of the currently available characteristic variable information. The failure prognostic model is finally used in a condition based decision model to support the replacement decision of the dynamic systems. A case example is examined to demonstrate the implementation and potential applications of the proposed failure prognostic algorithm and the condition-based replacement model.     

An inexact reasoning algorithm based on fuzzy rule matrix transformations

This paper presents an efficient algorithm for dealing with inexact reasoning where fuzzy production rules are used for knowledge representation. The finiteness of the algorithm is also analyzed by means of reachability trees.     

基于ActiveX控制的混凝土安全性专家系统

为了提高混凝土安全性专家系统的重用性以及系统以后升级与维护的方便,采用了ActiveX控制技术,并设计了系统的组成结构与子系统控件,介绍了设计方法,应用这种动态链接的方法可以使系统更容易开发、维护和扩展。