失效分析专家系统

对系统结构、失效分析知识的获取和表达及计算机推理进行了研究.该系统包括六个主要功能模块:知识库及其管理系统、知识获取、数据库及其管理系统、字典库及其管理系统、推理机、人机交互界面.失效分析知识的获取采用了通过文本及机器学习方法;知识的表达采用了框架、规则及面向对象综合表达方式.计算机推理则采用不确定性推理方法,具体采用了置信度方法.     

失效分析专家系统研究进展

计算机辅助失效分析专家系统对快速、准确地对机械产品进行失效分析具有重要意义.简要介绍了计算机辅助失效分析的研究现状与发展,重点介绍了作者在机械结构失效辅助诊断与分析专家系统方面的研究成果,提出了计算机辅助失效分析研究的关键技术为:合理的系统结构、领域知识的获取和计算机表达及计算机智能推理.还对计算机辅助失效分析研究中主要的关键技术进行了详细的探讨.     

不确定性推理理论在卫星故障检测和诊断中的应用

推理理论一般分为确定性推理理论和不确定性推理理论。传统的卫星故障检测和诊断应用的是确定性推理。然而，在卫星故障检测和诊断的实践中，仅使用确定性推理是很难对某些故障进行检测和诊断的，因为这时需要合情推理和容错能力。不确定性推理理论可以满足此要求。目前，航天领域的许多专家和实际工作者正致力于应用不确定性推理理论检测和诊断那些用确定性推理无法检测和诊断的故障。不确定性推理理论包括诸如包含度理论、粗糙集理论、证据推理理论、概率推理理论、模糊推理理论等。笔者研究的卫星故障检测和诊断的三种新方法，分别应用了包含度理论、粗糙集理论和证据推理理论。     

实例推理技术在航空部件失效分析中的应用

介绍了实例推理的基本原理,根据失效分析工作的特点,给出了部件失效分析的实例推理系统框架,并对存在的问题和今后的工作进行了探讨.     

断口反推疲劳应力及其在叶片断裂分析中的应用

介绍了断口反推疲劳应力的分类与方法，给出了疲劳条带间距反推疲劳应力的基本原理和方法，并着重研究了其在叶片断裂失效分析中的应用，结果表明，可采用疲劳条带间距对工程实际应用中叶片疲劳断裂时的应力进行断口反推，且该方法简单易行，误差较小。     

基于案例推理的企业项目管理决策支持系统的研究

基于案例推理方法的基本原理,将案例推理方法应用于项目管理辅助决策支持分析,论述了基于案例推理的项目管理决策支持系统中的一些关键步骤,研究了项目管理中项目任务问题案例的知识案例表达、属性权重分析和近邻方法相结合的检索模型,实现了案例检索和匹配,提出了辅助项目任务问题案例辅助决策支持系统的结构,开发了原型系统,并给出应用实例．     

基于B/S模式的弹药失效诊断评估系统研究

为了提高通用弹药产品可靠性评估的效率,实现产品失效及可靠性的系统化辅助分析评估,对通用弹药产品的失效过程进行研究,总结出产品的一般失效分析过程、失效诊断方法及弹药失效分析数据字典.在辅助分析系统中,提供了失效分析、可靠性评估、FMECA等模块.应用Java、JSP等语言,在B/S模式下进行设计开发,实现了对产品失效的过程分析及产品可靠性评估.优化可靠性统计分析的数学模型,改进评估参量算法,使评估工作效率提高30%以上,参数精度达到小数点后8位.     

基于Vague集与FTA方法的麻醉流程风险评估

麻醉是提高患者安全的首要环节。围手术期麻醉过程受大量随机、模糊和不确定性因素影响而呈现出多态性,传统故障树分析(FTA)在评估面向该类流程风险问题上存在明显缺陷。通过引入Vague集解决麻醉操作中失效事件多态性和失效概率不确定性,并依据有明显边界的麻醉操作阶段来构建面向流程的故障树模型,基于医院麻醉记录、医师经验、可靠性数据并结合TOPAZ模型,对围手术期麻醉流程进行了风险评估。实例验证该方法可行且有效,与现有方法比较分析发现该结果更好地刻画出失效事件随机性和不确定性。     

环境不确定性对创业资源整合情境下因果-效果推理决策逻辑的影响:创业网络的调节效应

目的新创企业在何种情况下更倾向于因果推理逻辑或效果推理逻辑是近年创业和效果推理两个领域的重要研究议题。本文研究市场、技术和制度三种环境不确定性对创业资源整合情境下因果-效果推理决策逻辑的影响及网络关系强度和关系质量的调节作用。方法运用SPSS和AMOS对305份有效问卷数据进行探索性因子分析、验证性因子分析、相关分析和回归分析。结果技术不确定性对因果-效果推理决策逻辑有显著负向影响,而制度不确定性对因果-效果推理决策逻辑有显著正向影响;网络关系强度和网络关系质量都对制度不确定性与因果-效果推理决策逻辑的正向关系中有调节效应。结论技术不确定性对因果推理决策逻辑有预测作用,而制度不确定性对效果推理决策逻辑有预测作用;网络关系强度越高,制度不确定性对效果推理决策逻辑的预测作用越强;网络关系质量越好,制度不确定性对效果推理决策逻辑的预测作用越强。     

机械故障诊断的推理规律研究

机械故障诊断对于设备的安全、连续运行和预知维修至关重要。作为故障诊断成功的关键，文章阐述了诊断中的诊断知识及其结构，介绍了专家的概念知识、方法知识和面向诊断对象知识，并用一种双向、互补的有机结构来刻划这些知识在诊断过程中的综合应用。将故障熵的概念引申为广义故障熵，并结合最小互熵原理，用于阐述诊断推理过程。分别用对数模型和Sigmoid模型来刻划机械故障诊断中的认知规律。通过某炼油厂的一个实际诊断案例对这两个模型进行了分析和对比研究。结果表明，两者均能近似地刻划认知过程，但Sigmoid模型更能准确地描述机械故障诊断推理的一般规律，更符合诊断过程的实际情况。     

Reliability assessment for failure-dependent and uncertain systems: A Bayesian network based on copula method and probability-box

Managing failure dependence of complex systems with hybrid uncertainty is one of the hot problems in reliability assessment. Epistemic uncertainty is attributed to complex working environment, system structure, human factors, imperfect knowledge, etc. Probability-box has powerful characteristics for uncertainty analysis and can be effectively adopted to represent epistemic uncertainty. However, arithmetic rules on probability-box structures are mostly used among structures representing independent random variables. In most practical engineering applications, failure dependence is always introduced in system reliability analysis. Therefore, this paper proposes a developed Bayesian network combining copula method with probability-box for system reliability assessment. There are four main steps involved in the reliability computation process: marginal distribution identification and estimation, copula function selection and parameter estimation, reliability analysis of components with correlations and Bayesian forward analysis. The benefits derived from the proposed approach are used to overcome the computational limitations of n-dimensional integral operation, and the advantages of useful properties of copula function in reliability analysis of systems with correlations are adopted. To demonstrate the effectiveness of the developed Bayesian network, the proposed method is applied to a real large piston compressor.     

Belief rule-based dependence assessment method under interval uncertainty

Human reliability analysis (HRA) is of great significance for probabilistic risk assessment, and the technique for human error rate prediction (THERP) has been widely applied to assess the dependence among HRA. However, uncertainties in analyst's judgment and experts' knowledge, especially interval uncertainty in analyst's judgment, have been ignored by existing methods. To this end, the belief rule-based system is employed to model uncertainties in experts' knowledge in this paper, and the interval belief distribution is used to model interval uncertainty in analyst's judgment. Then, a new belief rule-based dependence assessment method is proposed, and two case studies are used to illustrate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method could not only model uncertainty using belief rules and interval belief distributions, but also provide a novel and effective way for human reliability analysis.     

A performance evaluation of three inference engines as expert systems for failure mode identification in shafts

This paper aims to present performance evaluation of three different inference engines (rule based reasoning, fuzzy based reasoning and Bayesian based reasoning) for failure mode identification in shafts. This research was done with a focus on the validation cases and results after their use in failure cases from several industries where the three systems were tested under the same conditions.Each system was implemented using the same user interface and knowledge base, with different frameworks and techniques as follows: rule based inference reasoning (prolog, C#), Mamdani-fuzzy based reasoning (C, MATLAB®) and Bayesian based reasoning with a variable elimination algorithm (C, MATLAB®).The best performance was obtained using the Bayesian inference engine. The conditional probabilities give flexibility when evidence is not listed, while the fuzzy and classical IF-THEN systems depend on the rules in the inference engine.The process presented in this paper could be used for validation of any expert system or for comparison with other expert systems (inference engines) when the knowledge base is the same.     

A PROBABILISTIC APPROACH TO FAILURE PREDICTION OF FRP LAMINATED COMPOSITES

The failure of fiber-reinforced plastic (FRP) laminated composites is examined, taking into account statistical aspects of the basic strength properties of the material The purpose of the analysis is to establish simple rules and methodologies for failure prediction under specific reliability requirements. An analytical approach is developed for the prediction of failure under general in-plane loading, consisting of a functional expansion technique for the derivation of the cumulative distribution Junction of the failure condition. A semideterministic method is also examined, according to which the failure criterion is used in the usual deterministic way but with the basic strength values at a certain reliability level. The two methods are gradually built up by first examining their effectiveness on the failure prediction of unidirectional laminae. Results for the case of a unidirectional laminate are presented for the case of off-axis layers, and both methods are shown to yield results in fair agreement with Monte Carlo simulated ones and experimental data. For the more general case of multilayered composites, comparison of theoretical results with numerical simulated data and experimental ones reveals that both methods hold their effectiveness.     

2D C/SiC复合材料的可靠性评价

采用概率论和数理统计方法, 以研究分析2D C/SiC复合材料的弯曲强度分布规律为切入点, 比较了失效概率预测值与实验值, 用可靠度、 风险函数和可靠强度评价了该材料可靠性。通过线性回归分析和拟合优度检验得到正态、 对数正态和三参数Weibull分布模型均可表征其弯曲强度分布规律; 确定了该材料弯曲强度失效概率、 可靠度函数、 风险函数和可靠强度的数学模型中的参数, 可以预测给定强度条件和许用可靠度条件下的多种可靠性指标; 材料弯曲强度均值的三种模型预测值与实测值最大相对误差仅0.07%, 计算得到的失效概率曲线与实验弯曲强度的失效分布均符合很好。      

Modeling system behavior for risk and reliability analysis using KBARM

With advances in technology and the growing complexity of technological systems, the job of the reliability/system analyst has become more challenging as they have to study, characterize, measure and analyze the behavior of systems with the help of various traditional analytical (mathematical and statistical) techniques, which require knowledge of the precise numerical probabilities and component functional dependencies, information which is difficult to obtain. Even if data are available they are often inaccurate and are thus subject to uncertainty, i.e. historical records can only represent the past behavior and may be unable to predict the future behavior of the equipment. To cope with such situations, the knowledge‐based approximate reasoning methodologies (KBARMs) provide necessary help. Among them, the fuzzy and grey methodologies are the most viable and effective tools for coping with imprecise, uncertain and subjective information in a consistent and logical manner. In this paper, the authors present a methodological and structured approach (which makes use of both qualitative and quantitative techniques) to model, analyze and predict the failurebehavior of two units, namely the forming and press units of a paper machine, using KBARMs. Various system parameters such as repair time, failure rate, mean time between failures, availability and expected number of failures are computed to quantify the system behavior in terms of fuzzy, crisp and defuzzified values. Furthermore, a risk ranking approach based on fuzzy and grey relational analysis is discussed to prioritize various failure causes associated with the components in failure mode and effects analysis (FMEA). Copyright © 2007 John Wiley & Sons, Ltd.     

Application of Bayesian Methods for Age‐Dependent Reliability Analysis

In this article, the authors present a general methodology for age‐dependent reliability analysis of degrading or ageing components, structures and systems. The methodology is based on Bayesian methods and inference—its ability to incorporate prior information and on ideas that ageing can be thought of as age‐dependent change of beliefs about reliability parameters (mainly failure rate), when change of belief occurs not only because new failure data or other information becomes available with time but also because it continuously changes due to the flow of time and the evolution of beliefs. The main objective of this article is to present a clear way of how practitioners can apply Bayesian methods to deal with risk and reliability analysis considering ageing phenomena. The methodology describes step‐by‐step failure rate analysis of ageing components: from the Bayesian model building to its verification and generalization with Bayesian model averaging, which as the authors suggest in this article, could serve as an alternative for various goodness‐of‐fit assessment tools and as a universal tool to cope with various sources of uncertainty. The proposed methodology is able to deal with sparse and rare failure events, as is the case in electrical components, piping systems and various other systems with high reliability. In a case study of electrical instrumentation and control components, the proposed methodology was applied to analyse age‐dependent failure rates together with the treatment of uncertainty due to age‐dependent model selection. Copyright © 2013 John Wiley & Sons, Ltd.     

基于贝叶斯-GO综合法的快递配送系统可靠性研究

提出用贝叶斯-GO综合方法分析快递配送系统可靠性。首先综合考虑配送系统对时间、品质、服务可靠性的要求,详细分析配送工作流程及其影响因素,建立GO图;然后根据映射规则转换为贝叶斯网络,最后通过案例分析,利用贝叶斯网络正反向推理和敏感度分析,得到系统的薄弱环节及其主要影响因素。研究结果表明：贝叶斯-GO综合法克服了单独使用GO法逻辑运算复杂繁琐的缺点,能够快速找到快递配送系统的薄弱环节及其主要影响因素,其中收件子系统故障率最高,快递员的配送不及时、寄件损坏、丢失是主要影响因素。     

三级倒立摆的云控制方法及动平衡模式

文章提出了定性和定量之间转换的云模型的形式化表示方法,用来反映语言值中蕴涵的模糊性和随机性,给出云发生器的生成算法,解释多条定性推理规则同时被激活时的不确定性推理机制。利用这种智能控制方法有效地实现了单电机控制的一、二、三级倒立摆的多种不同动平衡姿态,显示其鲁棒性,并给出了详细试验结果。研究成果不仅可用于对太空飞行器以及机器人控制,而且对揭示定性定量转换规律和策略具有普遍意义。     

Uncertain Knowledge Reasoning Based on the Fuzzy Multi Entity Bayesian Networks

With the rapid development of the semantic web and the ever-growing size of uncertain data, representing and reasoning uncertain information has become a great challenge for the semantic web application developers. In this paper, we present a novel reasoning framework based on the representation of fuzzy PR-OWL. Firstly, the paper gives an overview of the previous research work on uncertainty knowledge representation and reasoning, incorporates Ontology into the fuzzy Multi Entity Bayesian Networks theory, and introduces fuzzy PR-OWL, an Ontology language based on OWL2. Fuzzy PR-OWL describes fuzzy semantics and uncertain relations and gives grammatical definition and semantic interpretation. Secondly, the paper explains the integration of the Fuzzy Probability theory and the Belief Propagation algorithm. The influencing factors of fuzzy rules are added to the belief that is propagated between the nodes to create a reasoning framework based on fuzzy PR-OWL. After that, the reasoning process, including the SSFBN structure algorithm, data fuzzification, reasoning of fuzzy rules, and fuzzy belief propagation, is scheduled. Finally, compared with the classical algorithm from the aspect of accuracy and time complexity, our uncertain data representation and reasoning method has higher accuracy without significantly increasing time complexity, which proves the feasibility and validity of our solution to represent and reason uncertain information.