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GDA的置信规则库参数训练的集成学习方法

目前对置信规则库(belief rule base,BRB)的研究主要针对单个BRB系统,然而单个BRB系统的推理性能不仅受参数取值的影响,而且当训练集分布不均衡或数据量较少时,容易导致参数训练不全面,从而使得推理结果所提供的决策信息存在局部性。通过引入Bagging算法和Ada Boost算法,分别与BRB相结合提出了基于梯度下降法(gradient descent algorithm,GDA)的置信规则库系统的集成学习方法,并分别应用于输油管道检漏、多峰函数的置信规则库训练,将多个BRB子系统集成,提高系统的推理性能。在实验中,以收敛精度和曲线拟合效果作为衡量指标来分析集成系统的性能,并将集成系统与其他单个BRB系统进行比较,实验结果表明BRB集成学习方法合理有效。     

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C-BRB模型的发动机运行状态监测方法

针对发动机运行状态监测过程中发动机内部多个因素之间相关性与建模方法可解释性问题,提出数据驱动下C-BRB方法.该方法首先通过样本数据计算发动机内部多个因素之间的Kendall秩相关系数,并确定具体Copula模型及参数λ,实现对多个因素之间相关性的测量;然后使用置信规则库(BRB)对发动机内部多个因素建模,在BRB推理...     

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。     

数据缺失的扩展置信规则库推理方法

数据驱动的扩展置信规则库专家系统能够处理含有定量数据或定性知识的不确定性问题.该方法已被广泛地研究和应用,但仍缺乏在不完整数据问题上的研究.鉴于此,针对不完整数据集上的问题,提出一种新的扩展置信规则库专家系统推理方法.首先提出基于析取范式的扩展规则结构,并通过实验讨论了在新的规则结构下,置信规则前提属性参考值个数对推理...     

基于置信规则库的海基系统性能退化机理分析与预测

海基系统性能退化机理分析和预测对于提高海基系统的生存能力具有重要意义,但需要考虑不确定条件下的多种类型信息,传统方法在处理海基系统的不确定性时效果欠佳,而置信规则库(BRB)作为证据推理方法中的知识库又无法同时处理参数精度优化和组合爆炸问题.对此,采用BRB参数与结构联合优化方法,建立双层优化的海基系统置信规则库最优决策结构,以AIC(Akaike information criterion)为外层模型优化目标,MSE(Mean square error)为内层模型优化目标,实现同时优化的目的.对比模型输出和实际输出,并采用支持向量机(SVM)进行实验,结果表明,采用具有最优决策结构的海基系统置信规则库建模不仅可以降低模型中规则的数量,也可提高建模精度,验证了所提出方法的有效性.     

基于平行多种群与冗余基因策略的置信规则库优化方法

置信规则库(Belief rule base, BRB)的参数学习和结构学习共同影响着置信规则库的建模精度和复杂度.为了提高BRB结构学习和参数学习的优化效率,本文提出了一种基于平行多种群(Parallel multi-population)策略和冗余基因(Redundant genes)策略的置信规则库优化方法.该方法采用平行多种群策略以实现对具有不同数量规则BRB同时进行优化的目的,采用冗余基因策略以确保具有不同数量规则的BRB能够顺利进行(交叉,变异等)相关优化操作.最终自动生成具有不同数量规则BRB的最优解,并得出帕累托前沿(Pareto frontier),决策者可以根据自身偏好和实际问题需求,综合权衡并在帕累托前沿中筛选最优解.最后以某输油管道泄漏检测问题作为示例验证本文提出方法的有效性,示例分析结果表明本文提出的方法可以一次生成具有多条规则BRB的最优解,并且可以准确绘制出帕累托前沿,为综合决策提供较强的决策支持.     

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.     

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base. The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model. However, due to the complexity of the milling system structure and the uncertainty of the milling failure index, it is often impossible to construct model expert knowledge effectively. Therefore, a milling system fault detection method based on fault tree analysis and hierarchical BRB (FTBRB) is proposed. Firstly, the proposed method uses a fault tree and hierarchical BRB modeling. Through fault tree analysis (FTA), the logical correspondence between FTA and BRB is sorted out. This can effectively embed the FTA mechanism into the BRB expert knowledge base. The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion. Secondly, evidence reasoning (ER) is used to ensure the transparency of the model reasoning process. Thirdly, the projection covariance matrix adaptation evolutionary strategies (P-CMA-ES) is used to optimize the model. Finally, this paper verifies the validity model and the method's feasibility techniques for milling data sets.     

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.     

Belief rule-based methodology for mapping consumer preferences and setting product targets

Rapid and accurate identification of consumer demands and systematic assessment of product quality are essential to success for new product development, in particular for fast moving consumer goods such as food and drink products. This paper reports an investigation into a belief rule-based (BRB) methodology for quality assessment, target setting and consumer preference prediction in retro-fit design of food and drink products. The BRB methodology can be used to represent the relationships between consumer preferences and product attributes, which are complicated and nonlinear. A BRB system can initially be established using expert knowledge and then optimally trained and validated using data generated from consumer or expert panel assessments or from tests and experiments. The established BRBs can then be used to predict the consumer acceptance of new products or set product target values in retro-fit design. The proposed BRB methodology is applied to the design of a lemonade drink product using real data provided by a sensory product manufacturer in the UK. The results show that the BRB methodology can be used to predict consumer preferences with high accuracy and to set optimal target values for product quality improvement.     

Optimization Models for Training Belief-Rule-Based Systems

A belief rule-base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule representation scheme is proposed to extend traditional IF-THEN rules. The belief rule expression matrix in RIMER provides a compact framework for representing expert knowledge. However, it is difficult to accurately determine the parameters of a belief rule base (BRB) entirely subjectively, particularly, for a large-scale BRB with hundreds or even thousands of rules. In addition, a change in rule weight or attribute weight may lead to changes in the performance of a BRB. As such, there is a need to develop a supporting mechanism that can be used to train, in a locally optimal way, a BRB that is initially built using expert knowledge. In this paper, several new optimization models for locally training a BRB are developed. The new models are either single- or multiple-objective nonlinear optimization problems. The main feature of these new models is that only partial input and output information is required, which can be either incomplete or vague, either numerical or judgmental, or mixed. The models can be used to fine tune a BRB whose internal structure is initially decided by experts' domain-specific knowledge or common sense judgments. As such, a wide range of knowledge representation schemes can be handled, thereby facilitating the construction of various types of BRB systems. Conclusions drawn from such a trained BRB with partially built-in expert knowledge can simulate real situations in a meaningful, consistent, and locally optimal way. A numerical study for a hierarchical rule base is examined to demonstrate how the new models can be implemented as well as their potential applications.     

组合评价方法在煤矿安全风险评估中的应用

研究煤矿安全风险准确评估问题,煤矿生产的复杂性导致煤矿事故的动态性、模糊性和随机性,且影响煤矿安全风险等级指标多,指标与风险等级之间呈复杂的非线性关系,导致传统评估方法的准确率低。为了提高煤矿安全风险评估的准确率,提出一种组合的煤矿安全风险评估方法。首先构建出煤矿安全风险评估指标体系,然后采用层次分析法计算各评估指标权重,且采用模糊方法建立判断矩阵,最后将其输入到BP神经网络学习建立煤矿安全风险评估模型。利用具体数据对模型性能进行了验证性测试。实验结果表明,相比较于其它评价方法,组合评价方法提高了煤矿安全风险评估的准确率,是一种有效的煤矿安全风险评估方法。     

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.     

老旧建筑安全鉴定辅助决策系统的初步开发

以老旧建筑的表观检测数据或震害调查结果为依据,利用区间分析方法、模糊综合评判模型、专家系统的构造思想等最新信息处理技术建立起整套的老旧建筑安全鉴定辅助决策系统(专家系统).系统按照检测管理步骤(现场监测→数据处理→专家评价),分为数据录入及管理、等级评定、专家评价三大模块.该系统不但可以帮助建筑物管理者处理大量的检测数据,评价既有建筑物的安全等级,还可以根据老旧建筑物的实际情况,给出专家的最佳管理方案.     

基于模糊攻防树和熵权法的工控系统脆弱性量化评估方法

为了提高脆弱性量化评估结果的可靠性,提出一种基于模糊攻防树和熵权法的工控系统脆弱性量化评估方法。该方法以攻击防御树为模型,首先将模糊集合理论与专家评价相结合,然后聚合多位专家对同一安全属模糊评价,在模糊聚合过程中利用模糊距离计算专家模糊评价的偏离度以提高模糊聚合的可靠性,并采用熵权法确定叶子节点量化过程中各安全属性的权重。最后计算叶子节点及攻击序列概率。案例分析表明,该方法能有效提高评估结果的可靠性,为工控系统信息安全防护提供重要依据。     

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.