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...     

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推理过程中,每条激活规则的综合匹配度由Copula模型对该规则中各前提属性的匹配度进行计算得到,并利用证据推理(ER)算法对所有激活规则进行融合得到输出。实例结果表明,所提方法在推理发动机传感器数据上具有较高的精度。     

基于置信规则库推理的装备战损等级评定

针对复杂战场环境下战损数据的多源性和不确定性,本文根据战损等级评定的非线性特点,提出了一种基于置信规则库(belief rule base,BRB)和证据推理(evidential reasoning,ER)的装备战损等级评定方法.首先,在战损等级评定影响因素分析的基础上,建立了一种新的融合多种特征信息的BRB-ER战...     

电子元器件加速退化寿命评估方法研究

基于退化轨迹的评估方法是退化型产品进行可靠性评估的主要方法,适合于对具有退化失效机理的高可靠长寿命产品进行可靠性评估;基于退化轨迹的可靠性评估方法首先选取合适的退化轨迹模型,利用退化数据对退化轨迹进行模型拟合得到模型参数,然后根据退化轨迹外推得到伪失效寿命,最后基于伪失效寿命利用最小二乘法进行统计分析确定产品的失效分布,并通过假设检验的方法选择拟合度最优的分布;本文以大功率开关的加速退化试验数据为例进行了分析和说明.     

基于非确定性推理的网构软件服务质量动态评估方法

提出了网构软件环境下一种基于非确定性推理的构件服务质量动态评估方法.该方法基于贝叶斯网络,其主要特点在于考虑了用户对构件的QoS需求,可以预测在用户多种QoS需求下采用分级策略的构件服务能力,支持评估模型的动态更新,提高了评估结果的准确性.在自主开发的服务协同总线(Once-SCB)平台上进行了应用与验证,结果表明,该评估模型准确、有效,可以在用户多种QoS需求下为其选择最为合适的构件.     

振动环境下MEMS加速度计的可靠性评估

在MEMS加速度计加速寿命试验及加速性能退化试验研究的基础上,对MEMS加速度计在振动环境下的可靠性技术进行了研究.通过理论分析MEMS加速度计在振动环境下的失效模式和失效机理,结合具体的试验条件设计了加速度计加速寿命试验及加速性能退化试验方案,并对MEMS加速度计在振动环境下的失效数据分别进行了加速寿命可靠性评估及加速性能退化可靠性评估.研究表明,两种评估方法得到的评估结果基本一致;加速性能退化评估方法适用于MEMS加速度计在振动环境中的可靠性研究,且该方法简捷、正确可行、节省试验费用,为MEMS加速度计在实际应用中提供了重要的参考依据.     

基于证据推理规则的无线传感器网络数据可靠性分析

无线传感器网络(Wireless Sensor Network, WSN) 在精密工程中具有广泛的应用,对数据精度的要求严苛,因此对WSN可靠性进行评估具有实用价值。本文以WSN实际监测数据为研究对象,通过对WSN数据提取特征,选取所测环境数据的时间相关性和空间相关性以及节点电压为可靠性指标,提出了基于证据推理规则(Evidential reasoning rule, ER)的WSN数据可靠性评估模型。该模型采用变异系数法和基于距离的方法,确定评估指标权重和可靠度,以基于规则的方法将指标数据统一成置信分布形式。利用证据推理规则对指标和参数进行融合,得到WSN数据可靠性状态。最后,通过实例分析验证该模型的有效性。     

基于寿命分布模型的数控装置可靠性定量评估方法

由于数控装置的使用周期长、故障次数少、分布模型不确定等特点,使得可靠性测试与评估工作难以实施.针对该问题,本文引入了基于数理统计的方法,根据已知的产品故障失效时间,通过假设检验确定产品寿命分布模型.本文以该模型为基础,通过引入加速寿命试验的方法,在保证失效机理不变的情况下,极大缩短了可靠性试验时间,完成了数控装置可靠性试验和定量计算,从而使得数控装置的可靠性测试与评估工作变得切实可行.     

冲击应力下高g加速度传感器的加速寿命评估

由于高g加速度传感器在航空、航天、国防等领域的广泛应用,其可靠性成为MEMS器件商业化过程中一个重要问题。本文针对快速准确的评估高g加速度传感器的寿命这一问题,提出了冲击应力下高g加速度传感器的加速寿命评估方法。通过分析冲击载荷作用于加速度传感器时产生的应力波及故障树分析法,确定高g加速度传感器的主要失效模式和敏感环境应力;采用恒定冲击应力试验得到高g加速度传感器的失效次数;选择正态分布和Weibull分布理论验证加速试验过程失效机理的一致性;利用逆幂律模型建立高g加速度传感器的可靠度评估模型,并外推出高g加速度传感器在规定应力下的可靠度函数。实践表明,该方法简捷、正确可行,为高g加速度传感器在实际应用中提供了重要的参考依据。     

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.     

A reasoning method for a ship design expert system

Abstract: The ship design process is a highly data‐oriented, dynamic, iterative and multi‐stage algorithm. It utilizes multiple abstraction levels and concurrent engineering techniques. Specialized techniques for knowledge acquisition, knowledge representation and reasoning must be developed to solve these problems for a ship design expert system. Consequently, very few attempts have been made to model the ship design process using an expert system approach. The current work investigates a knowledge representation–reasoning technique for such a purpose. A knowledge‐based conceptual design was developed by utilizing a prototype approach and hierarchical decompositioning. An expert system program called ALDES (accommodation layout design expert system) was developed by using the CLIPS expert system shell and an object‐oriented user interface. The reasoning and knowledge representation methods of ALDES are explained in the paper. An application of the method is given for the general arrangement design of a containership.     

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.     

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.     

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.     

Iterative learning belief rule-base inference methodology using evidential reasoning for delayed coking unit

The belief rule-base inference methodology using evidential reasoning (RIMER) approach has been proved to be an effective extension of traditional rule-based expert systems and a powerful tool for representing more complicated causal relationships using different types of information with uncertainties. With a predetermined structure of the initial belief rule-base (BRB), the RIMER approach requires the assignment of some system parameters including rule weights, attribute weights, and belief degrees using experts’ knowledge. Although some updating algorithms were proposed to solve this problem, it is still difficult to find an optimal compact BRB. In this paper, a novel updating algorithm is proposed based on iterative learning strategy for delayed coking unit (DCU), which contains both continuous and discrete characteristics. Daily DCU operations under different conditions are modeled by a BRB, which is then updated using iterative learning methodology, based on a novel statistical utility for every belief rule. Compared with the other learning algorithms, our methodology can lead to a more optimal compact final BRB. With the help of this expert system, a feedforward compensation strategy is introduced to eliminate the disturbance caused by the drum-switching operations. The advantages of this approach are demonstrated on the UniSim? Operations Suite platform through the developed DCU operation expert system modeled and optimized from a real oil refinery.     

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.