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。     

出租车乘车概率预测的置信规则库推理方法

出租车乘车概率预测中存在数据量级大,底层属性类型多,预测信息不确定的问题。鉴于此,整合大规模轨迹数据范畴中现有的挖掘算法对出租车GPS数据和路网数据进行离线处理;将多类型的不确定性数据转换为具有置信结构的规则形式,并以此构建置信规则库;通过置信规则库推理方法（belief rule-base infer-ence methodology using evidential reasoning,RIMER）在线预测路网道路上各个地点的乘车概率。以北京市2012年11月某天的出租车GPS数据为例说明该在线预测方法的应用。实验结果表明,该预测方法具有较高的实时性和准确性。     

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。     

置信规则库规则约简的粗糙集方法

针对置信规则中规则数的"组合爆炸"问题,目前的解决方法主要是基于特征提取的规则约简方法,有效性依赖于专家知识.鉴于此,提出基于粗糙集理论的无需依赖规则库以外知识的客观方法,按照等价类划分思想逐条分析置信规则,进而消除冗余的候选值.最后,以装甲装备能力评估作为实例进行分析,分别从规则约简数、决策准确性方面与具有代表性的主观方法进行对比,结果表明,所提出方法是有效可行的,且优于现有规则约简主观方法.     

基于规则聚类和参数学习的扩展置信规则库推理模型

扩展置信规则库(EBRB)中的规则数量和参数取值共同影响EBRB推理模型的决策准确性和计算效率. 基于此,提出一种基于规则聚类和参数学习的改进EBRB推理模型,称为RCPL-EBRB模型.所提出模型的基本原理如下:首先,依据密度聚类分析对EBRB进行规则聚类来识别EBRB中无效的扩展置信规则和优化传统EBRB的建模过程;然后,以聚类所得到的规则簇(即Sub-EBRB)进行参数学习和规则推理,保证激活规则集合的一致性,从而提高RCPL-EBRB模型的决策准确性和计算效率;最后,引入非线性函数拟合和基准分类问题数据集开展模型的有效性检验和参数灵敏度分析.实验结果表明,所提出RCPL-EBRB模型比现有EBRB推理模型和传统机器学习方法具有更高的决策准确性.     

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。     

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.     

基于置信规则库的工业控制系统安全状态评估

当前工业控制系统存在严重安全问题,针对现有工业控制系统安全状态评估模型存在的不足,提出一种基于置信规则库(BRB)专家系统的工业控制系统安全状态评估方法.该方法首先利用置信规则库专家系统将工业控制系统中定性知识与定量监测数据相结合.然后采用证据推理(ER)算法进行知识推理,并对所建立的BRB模型初始参数进行优化.最后以...     

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.     

利用置信规则库构建WSN节点故障检测模型

在无线传感器网络(wireless sensor network, WSN)节点故障检测领域的研究过程中,故障检测准确率会受节点数据的不确定性和专家知识模糊性的影响。针对这一问题,本文提出了一种基于置信规则库(belief rule base, BRB)的WSN节点故障检测方法。首先,根据WSN工作原理及节点工作特性描述WSN节点故障检测过程;然后,从空间和时间2个维度对节点数据提取特征,建立基于空间和时间相关性的WSN节点故障检测模型;最后,利用Intel Lab Data无线传感器数据集进行案例研究以验证模型的有效性。结果证明,本文方法能够统筹利用专家知识和节点数据实现WSN节点故障检测。     

基于聚类集成和激活因子的扩展置信规则库推理模型

规则约减和规则激活是扩展置信规则库(EBRB)推理模型优化研究中的两个重要方向.然而,现有研究成果大多存在方法参数确定主观性强和计算复杂度高等不足.为此,通过引入聚类集成和激活因子提出改进的EBRB推理模型,称为CEAF-EBRB模型.该模型先基于聚类集成对历史数据进行多次的数据聚类分析,再以簇为单位将所有历史数据生成扩展置信规则;同时,通过激活因子修正个体匹配度计算公式以及离线的方式计算激活因子取值,以确保高效地激活一致性的规则.最后,在非线性函数拟合、模式识别、医疗诊断等常见问题中验证了所提CEAF-EBRB模型的可行性和有效性,从而为决策者提供更准确的决策支持.     

Inference analysis and adaptive training for belief rule based systems

Belief rule base (BRB) systems are an extension of traditional IF-THEN rule based systems and capable of capturing complicated nonlinear causal relationships between antecedent attributes and consequents. In a BRB system, various types of information with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. For a set of inputs to antecedent attributes, inference in BRB is implemented using the evidential reasoning (ER) approach. In this paper, the inference mechanism of the ER algorithm is analyzed first and its patterns of monotonic inference and nonlinear approximation are revealed. For a practical BRB system, it is difficult to determine its parameters accurately by using only experts’ subjective knowledge. Moreover, the appropriate adjustment of the parameters of a BRB system using available historical data can lead to significant improvement on its prediction performance. In this paper, a training data selection scheme and an adaptive training method are developed for updating BRB parameters. Finally, numerical studies on a multi-modal function and a practical pipeline leak detection problem are conducted to illustrate the functionality of BRB systems and validate the performance of the adaptive training technique.     

基于变速粒子群优化的置信规则库参数训练方法

针对置信规则库(BRB)中参数优化模型的求解问题,引入群智能算法中的粒子群优化(PSO)算法,提出一种新的参数训练方法。将参数优化模型求解问题转换为带约束条件的非线性优化问题,在迭代寻优时限制粒子在搜索空间中,对失去速度的粒子重新赋予速度,维持种群中粒子多样性,从而实现参数训练。在输油管道检漏问题仿真实验中,训练后系统的平均绝对误差(MAE)为0.166478。实验结果表明,所提方法有理想的收敛精度,可用于置信规则库参数训练。     

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.