Reliability Analysis of Electrical System of CNC Machine Tool Based on Dynamic Fault Tree Analysis Method

The electrical system of CNC machine tool is very complex which involves many uncertain factors and dynamic stochastic characteristics when failure occurs.Therefore,the traditional system reliability analysis method,fault tree analysis(FTA)method,based on static logic and static failure mechanism is no longer applicable for dynamic systems reliability analysis.Dynamic fault tree(DFT)analysis method can solve this problem effectively.In this method,DFT first should be pretreated to get a simplified fault tree(FT);then the FT was modularized to get the independent static subtrees and dynamic subtrees.Binary decision diagram(BDD)analysis method was used to analyze static subtrees,while an approximation algorithm was used to deal with dynamic subtrees.When the scale of each subtree is smaller than the system scale,the analysis efficiency can be improved significantly.At last,the usefulness of this DFT analysis method was proved by applying it to analyzing the reliability of electrical system.

The Dynamic-to-Static Conversion of Dynamic Fault Trees Using Stochastic Dependency Graphs and Stochastic Activity Networks

In this paper a new modeling framework for the dependability analysis of complex systems is presented and related to dynamic fault trees (DFTs). The methodology is based on a modular approach: two separate models are used to handle, the fault logic and the stochastic dependencies of the system. Thus, the fault schema, free of any dependency logic, can be easily evaluated, while the dependency schema allows the modeler to design new kind of non-trivial dependencies not easily caught by the traditional holistic methodologies. Moreover, the use of a dependency schema allows building a pure behavioral model that can be used for various kinds of dependability studies. In the paper is shown how to build and integrate the two modular models and convert them in a Stochastic Activity Network. Furthermore, based on the construction of the schema that embeds the stochastic dependencies, the procedure to convert DFTs into static fault trees is shown, allowing the resolution of DFTs in a very efficient way.

Study on System Failure Probability Model Based on Dynamic Fault Tree

Aiming at the characteristics of complex logic relation and multiple dynamic gates in system,its failure probability model is established based on dynamic fault tree. For the multi-state dynamic fault tree,it can be transferred into Markov chain with continuous parameters. The state transfer diagram can be decomposed into several state transfer chains,and the failure probability models can be derived according to the lengths of the chains. Then,the failure probability of the dynamic fault tree analysis(DFTA) can be obtained by adding each chain's probability. The failure probability calculation of DFTA based on the continuous parameter Markov chain is proposed and proved. Given an example,the analytic method is compared with the conventional methods which have to solve the differential equation. It is known from the results that the analytic method can be applied to engineering easily.

Dynamic Fault Tree Analysis for Explosive Logic Network with Two-Input-One-Output

The explosive logic network( ELN) with two-input-oneoutput was designed with three explosive logic gap null gates. The time window of the output of the ELN was given,after which the dynamic fault tree analysis was implemented. Two dynamic failure modes of the ELN were obtained,and then their own Markov transition processes were established. After that,the probability of failure was calculated from the corresponding state transition diagram. The reliability of the ELN which was in different length of time under the ambient incentive was then analyzed. Based on the above processing,the reliability of the ELN can be improved.

Qualitative analysis for state/event fault trees using formal model checking

A state/event fault tree(SEFT)is a modeling technique for describing the causal chains of events leading to failure in software-controlled complex systems.Such systems are ubiquitous in all areas of everyday life,and safety and reliability analyses are increasingly required for these systems.SEFTs combine elements from the traditional fault tree with elements from state-based techniques.In the context of the real-time safety-critical systems,SEFTs do not describe the time properties and important timedependent system behaviors that can lead to system failures.Further,SEFTs lack the precise semantics required for formally modeling time behaviors.In this paper,we present a qualitative analysis method for SEFTs based on transformation from SEFT to timed automata(TA),and use the model checker UPPAAL to verify system requirements’properties.The combination of SEFT and TA is an important step towards an integrated design and verification process for real-time safety-critical systems.Finally,we present a case study of a powerboat autopilot system to confirm our method is viable and valid after achieving the verification goal step by step.

Fault Tree+软件在长输天然气管道定量风险分析中的应用

为了确定影响长输天然气管道系统发生事故的各种因素,减少事故发生所造成的损失,保证管道安全运行,借助Fault Tree+软件对天然气管道系统进行了定量风险分析。根据相关资料,建立了长输天然气管道系统的故障树,明确了44个基本事件的概率,并运用Fault Tree+软件分析、计算功能,确定了管道穿孔、断裂等中间事件以及管道失效顶事件的概率。再以"断裂泄漏"事故开展事件树分析,建立"立即点燃"、"延迟点燃"等事件,并推导了可能导致的所有后果及概率,计算出"延迟点燃"事件没有发生以及发生"爆炸"事故的概率。最后以经济损失来度量"断裂泄漏"各后果事件的失效风险,累加每个后果事件的经济损失,得到某段天然气管道"断裂泄漏"事故的风险值。

Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant

Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph(DUCG)has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree(FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm(GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.

Fuzzy fault tree analysis of roller oscillating tooth gear drive

Conventional fault tree and reliability analysis do not reflect the characteristics of basic events as non stationary and ergodic process. To overcome these drawbacks, theory of fuzzy sets is employed to run fault tree analysis(FTA) of roller oscillating tooth gear drive(ROTGD), the relative frequencies of basic events are considered as symmetrical normal fuzzy numbers, from the logical relationship between different events in the fault tree and fuzzy operators AND and OR, fuzzy probability of top event is solved. Finally, an example is given to demonstrate a real ROTGD system.

Analytical Model and Algorithm of Fuzzy Fault Tree

In the past, the probabilities of basic events were described as triangular or trapezoidal fuzzy number that cannot characterize the common distribution of the primary events in engineering, and the fault tree analyzed by fuzzy set theory did not include repeated basic events. This paper presents a new method to analyze the fault tree by using normal fuzzy number to describe the fuzzy probability of each basic event which is more suitably used to analyze the reliability in safety systems, and then the formulae of computing the fuzzy probability of the top event of the fault tree which includes repeated events are derived. Finally, an example is given.

Irrational-Behavior-Proof Conditions for Stochastic Games over Event Trees

In this paper,the irrational-behavior-proof conditions in a class of stochastic dynamic games over event trees are presented.Four kinds of irrational-behavior-proof conditions are proposed by the imputation distribution procedure,and their relationships are discussed.More specific properties for the general transformation of characteristic functions are developed,based on which,the irrational-behavior-proof conditions are proved to be true in a transformed cooperative game.

Reliability Analysis for Complex Systems Based on Dynamic Evidential Network Considering Epistemic Uncertainty

Fault tolerant technology has greatly improved the reliability of modern systems on one hand and makes their failure mechanisms more complex on the other.The characteristics of dynamics of failure,diversity of distribution and epistemic uncertainty always exist in these systems,which increase the challenges in the reliability assessment of these systems significantly.This paper presents a novel reliability analysis framework for complex systems within which the failure rates of components are expressed in interval numbers.Specifically,it uses a dynamic fault tree(DFT)to model the dynamic fault behaviors and copes with the epistemic uncertainty using Dempster-Shafer(D-S)theory and interval numbers.Furthermore,an approach is presented to convert a DFT into a dynamic evidential network(DEN)to calculate the reliability parameters.Additionally,a sorting method based on the possibility degree is proposed to rank the importance of components represented by interval numbers in order to obtain the most critical components,which can be used to provide the guidance for system design,maintenance planning and fault diagnosis.Finally,a numerical example is provided to illustrate the availability and efficiency of the proposed method.

激光自适应光学系统可靠性改进效果评估

随着自适应光学技术在激光领域的发展,工程上以经典自适应光学(AO)系统为基础,增加了多种基于软件监测和硬件保护的改进措施以保证激光AO系统稳定连续出光。面对结构复杂度提升带来的可靠性挑战,如何构建系统失效模型对激光AO系统可靠性进行评估,成为影响激光AO系统发展的重要一环。本文以激光光稳净化AO系统为例,提出使用动态故障树方法对激光AO系统可靠性进行评估,根据设备间动态关系建立动态故障树(DFT),结合厂家信息、疲劳寿命试验与历史数据估计得到底事件失效率,使用二元决策图和马尔可夫模型求解得到DFT的可靠性参数。使用DFT分析增加改进措施的AO系统可靠运行时间,结果相对于基本故障树获得了十倍以上的提高。实际系统调试期间,在预计的可靠运行时间内未发生自因故障,与DFT估计结果一致。验证了应用DFT方法评估增加改进措施后的激光AO系统可靠性更准确。

基于动态贝叶斯网络的蒽醌法制备双氧水工艺风险评估

为降低因蒽醌法制备双氧水而引起的火灾爆炸风险,从蒽醌法制备双氧水工艺中选取危险性较大的萃取净化工序展开风险评估。首先,分析该工序的危险源,采用Freefta软件建立事故树模型,在该模型的基础上使用GeNIe软件绘制事故树转换的动态贝叶斯网络模型;然后,运用专家打分法和模糊分析法计算模型中基本事件的先验概率,运用GeNIe软件在设定的前置条件下计算模型中基本事件的后验概率;最后,比较先验、后验概率之间的变化幅度,确定重要基本事件,揭示引起火灾爆炸的危险源并制定应急处置技术。结果表明:降解物与杂质造成污染、工艺副反应产生并增多、催化剂失效以及双氧水自身化学性质活跃4种重要危险源对萃取净化工序火灾爆炸影响最大;从阻止火灾蔓延与液体疏通的角度针对重要危险源制定应急处置技术效果较好。

基于融合系统方法的飞机复杂系统安全性分析

面对日益复杂的飞机系统,传统的安全性分析方法对复杂系统间的不安全交互行为和危险源的识别能力不足。为有效评价持续适航阶段的飞机系统安全性,提出了一种融合系统理论过程分析(system theory process analysis,STPA)和动态故障树(dynamic fault tree,DFT)的改进的STPA安全性分析方法和评价模型。模型采用STPA识别出不安全控制行为和致因因素,并将其与动态故障树分析方法相融合,以事故致因理论优化致因分析方法,计算得出不安全控制行为发生概率并确定系统潜在危险的关键致因因素。以飞机起落架系统为例进行分析验证,结果表明,改进后的STPA分析方法可以准确地对系统危险进行识别和分析,为持续适航阶段的安全性分析提供支持。

基于事故树的云机房网络安全态势自动化预测系统

为全面掌握云机房网络安全的运行状态,预测网络的安全态势,该文设计基于事故树的云机房网络安全态势自动化预测系统。该系统的云机房模块采用日志类传感器采集云机房网络数据,并存储至数据管理模块中,功能模块以存储的数据为依据,通过时间窗口检测存储数据中的异常数据流,采用逐级量化方法计算云机房网络危险度后,利用事故分析法计算云机房网络安全事件重要度,依据该结果即可分析网络未来的变化情况。测试结果可知,该系统具有全面的网络运行相关数据采集能力,网络安全态势的量化效果较好,决定系数结果均在0.022以下;有效实现不同目的IP地址网络的风险态势预测量化分析,可靠掌握云机房网络的安全态势情况。

ZDY6500LQK钻机电液控制系统故障分析

ZDY6500LQK是在煤矿井下大量推广应用的一款电控钻机,其远距离遥控操作减轻了工人劳动强度,提高了安全性。但随着设备的长期使用,钻机电液控制系统难免出现故障。为了快速、准确地定位故障原因和迅速处理故障,建立了以遥控发射器调节给进压力时压力显示异常为顶部事件的故障树模型。结合实际生产,定量分析了基础事件发生的概率。结合大概率基础事件的定性分析,从维护保养和设计优化两方面提出了解决方案,为钻机电液控制系统的改进和优化提供参考。

事件故障状态量子博弈过程的参与者收益研究

为了解事件故障状态量子博弈过程中参与者收益随各影响因素的变化情况,提出在空间故障树(Space Fault Tree, SFT)框架内,以事件故障状态为对象,对参与者收益进行研究。事件故障状态使用量子态表示,管理者和操作者的不同行为对事件故障状态的作用使用博弈表示。考虑因素包括安全产出价值、安全收益分配系数、安全措施成本。研究了事件故障状态与量子博弈的关系;纠缠与非纠缠态下的参与者收益;参与者收益受到各因素影响的特征等。研究得到了管理者和操作者考虑纠缠和非纠缠态的收益函数。结合SFT理论方法,提出了针对收益的因素重要度、因素联合重要度、收益风险区和安全区、因素区域重要度。理论上SFT可用于量子博弈参与者收益的分析。也论述了使用因素空间理论解决该问题的可能性。

基于DTBN与T-S故障树的电池热管理系统可靠性分析

提出一种评估电动汽车动力电池热管理系统的可靠性分析算法。构建了电池热管理系统T-S动态故障树,转化为离散时间Bayes网络(DTBN)模型。同时将T-S动态门规则转化为网络节点的条件概率表。依据所建可靠性模型和部件的故障率,计算得到动力电池热管理系统在任务时间内的故障概率值为0.453,并获得各部件的后验概率、概率重要度和关键重要度。结果表明:对比Monte Carlo仿真方法,本方法的故障概率计算值误差小于5%,概率重要度靠前的是单体电池温度传感器、电池冷却液泵、电池冷却液管路等部件。该方法能克服传统故障树分析难以构建Bayes网络条件概率表等问题。

基于超限事件的航班运行风险动态评估

为降低航班的运行风险,提高运行的安全水平,通过基于SPRINT的动态决策树算法,研究航班运行风险的动态评估。以某航空公司A320机队2016年3月份发生频率最高的五类QAR超限事件作为评价指标,分析飞行监控标准的风险阈值与超限事件的风险度,进而将航班运行风险划分为低风险、中等风险、高风险三类风险级别。通过改进SPRINT传统决策树算法,使其适应不断产生的航班运行风险数据,在旧树的基础上不断更新生成分类准确度更高的决策树。结果表明:该系统可通过对航班运行风险数据的准确分类,实现航班运行风险级别的评定,对提高航班运行安全有重要意义。

基于动态监测与事故树分析的超大深基坑风险分析方法研究

为对城市特大交通枢纽超大深基坑工程进行动态风险等级定量分析,提出风险事故树与基坑动态监测数据相结合的超大深基坑工程风险分析方法。列举基坑工程典型的各类风险源,绘制风险事故树并分析其基本事件结构重要度,将风险影响程度高的监测项目作为风险动态评价指标,同时考虑监测项目的动态评价因素和静态评价因素,将主因素突出法和综合评价分析法相结合。方法应用于北京城市副中心站特大交通枢纽超大深基坑工程项目中。研究结果表明:可有效避免获取事故树底事件发生概率的困难和单一使用综合评价分析法对基坑风险评价的失真影响,在实际工程中能够得到合理的基坑风险等级,具有较好的实用性和可操作性。研究结果可为超大深基坑工程风险分析提供参考。