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system reliability analysis method,fault tree analysis(FTA)method,based on static logic and static failure mechanism is no longer applicable for dynamic systems reliability analysis.Dynamic fault tree(DFT)analysis method can solve this problem effectively.In this method,DFT first should be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.

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.

基于TREE-LSTM算法的船舶汽轮机组变负荷故障诊断

针对船舶汽轮机组变负荷过程故障诊断中的耦合参数时序特征难以捕捉以及正常参数变动的干扰等问题,引入TREE-LSTM神经网络模型以实现复杂非线性系统动态数据分类。首先建立某船舶汽轮机组仿真模型,分析并进行故障仿真;随后进行数据预处理与特征工程;最后训练TREE-LSTM模型进行故障诊断,并与SVM、LSTM等模型进行比较。TREE-LSTM模型对于船舶汽轮机组变负荷过程的故障诊断正确率为98.7%,正确率最高。由于引入时间序列与复杂神经网络拓扑结构,TREE-LSTM在处理非线性系统动态数据分类问题时效果更好。

Modular solution of dynamic multiple-phased systems

A new modular solution to the state explosion problem caused by the Markov-based modular solution of dynamic multiple-phased systems is proposed. First, the solution makes full use of the static parts of dynamic multiple-phased systems and constructs cross-phase dynamic modules by combining the dynamic modules of phase fault trees. Secondly, the system binary decision diagram （BDD） from a modularized multiple- phased system （MPS）is generated by using variable ordering and BDD operations. The computational formulations of the BDD node event probability are derived for various node links and the system reliability results are figured out. Finally, a hypothetical multiple-phased system is given to demonstrate the advantages of the dynamic modular solution when the Markov state space and the size of the system BDD are reduced.

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DFTA的地铁车站级综合监控系统可靠性分析

地铁综合监控系统是整个地铁系统可靠安全运行的重要保障,由于其具有可修性、功能相关性、顺序相关性、容错性及冗余性等特点,本文采用动态故障树分析法(DFTA)对其进行可靠性分析。在引入新的动态可靠性指标的基础上,采用直接计算法和Markov矩阵迭代法求解,最后引入分层迭代方法对DFTA进行改进,可减小运算量,使得这些动态指标可应用于地铁综合监控系统整个动态故障树的可靠性评估。本文建立某典型地铁综合监控系统的车站级综合监控系统(SMCS)及其3个子系统的DFTA模型,研究3个子监控系统互联对整个车站级监控系统可靠性的影响。研究分析结果表明,动态故障树分析法在对地铁综合监控系统可靠性的分析中能够考察各子系统互联对系统可靠性的影响,具有较强的实用性。

基于ET-DFT分层模型的复杂系统动态概率安全评价方法研究

为充分体现复杂系统的动态行为对其可靠性的影响,减少系统动态故障概率计算时的工作量,应用层次化分析思想,综合事件树(ET)和动态故障树(DFT),提出基于ET-DFT模型的动态概率安全评价(DPSA)方法。应用二元决策图(BDD)和马尔科夫链(MC)分别对静态故障树和DFT进行求解,给出将静态故障树和DFT向BDD及马尔科夫链转移图的转化算法,以及系统各状态概率的求解方法。通过应用实例的分析验证,得到系统静态模块定量的发生概率及动态模块各个状态概率随时间变化的曲线,经综合求解得到系统故障发生的概率,实现对系统较为精确的DPSA。

基于层次分析法的DFT城市综合管廊盾构施工风险评价研究

随着城市建设现代化水平的提高,许多城市采用盾构施工方法来建设综合管廊。传统的事故树分析方法无法解决现代化城市综合管廊建设施工风险的复杂性问题。以管廊盾构施工导致的地面沉降事故为例,采用层次分析方法建立管廊盾构施工的动态事故树(DFT),并定性分析得出工程材料合格性、管片规格匹配性、采取相应的补救措施对管廊盾构施工导致地面沉降事故的影响程度较大,同时通过案例分析证明了采用DFT来分析城市综合管廊盾构施工过程中的危险因素具有可行性,可为城市综合管廊施工风险分析与评价提供新的思路。

基于贮备门DFT的飞控系统可靠性分析方法

针对现有大型客机电传(FBW)飞行控制系统(FCS)的体系结构及组成,运用动态故障树分析(DFTA)方法对系统进行可靠性分析;为系统所包含的贮备门事件(spare gates)建立了可靠性代数模型;该法较好地解决了子系统的分布多样问题,并且在逻辑符号系统基础上,推演出系统可靠性冗余分析的量化形式;通过对一个典型的电传飞控系统案例的研究,证明该种方法在工程中的有效性。

基于DFTA的电梯制动器可靠性分析

电梯制动器是电梯最重要的安全部件之一,故研究其可靠性具有重要意义。首先分析了电梯制动器的故障机理并建立了动态故障树模型,其次利用二元决策图和马尔科夫方法对故障树进行了分析计算,得到了影响系统可靠性的相关指标以及制动器的薄弱环节,最后提出了改进措施和建议,为电梯制动器的设计、维修保养及检验提供了科学依据。

基于动态故障树的交通信号灯监测系统故障分析

针对交通灯故障无法及时处理影响交通效率与安全的问题,设计了基于ZigBee技术的监测终端,根据监测系统中部件发生故障的时序关系建立动态故障树模型,分别采用二元决策图法BDD(binary decision diagrams)和马尔科夫模型对静态子树和动态子树进行定量分析,确定监测系统底事件对系统整体的概率重要度,计算得到导致交通灯故障的主要原因为光纤传感器位置移动和灯珠寿命到期。使用动态故障树法设计交通灯监测系统为工作人员快速分析故障原因提供了理论依据。

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T-S动态故障树的桁架机器人系统可靠性分析

为了提高桁架机器人系统的动态可靠性,并且在发生故障时提升排查故障的效率,提出一种对桁架机器人系统动态可靠性分析的方法。首先,对桁架机器人系统建立T-S动态故障树;然后,把T-S动态故障树和T-S动态门规则分别转化为离散时间贝叶斯网络(DTBN)与相应网络节点的条件概率表,根据DTBN正反向推理分别计算得到各时间段以及任务时间内系统失效概率和根节点后验概率,并获得各根节点的T-S概率重要度、T-S关键重要度和灵敏度;最后,应用Monte Carlo仿真法进行验证,结果显示相对误差为0.29%,证明所提方法可行。给出当系统发生故障时应优先排查的零部件,并确定了系统薄弱环节,得出应优先提高可靠度的零部件,为桁架机器人系统的动态可靠性分析提供了理论依据。

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DFT的飞机电源网络化故障诊断方法

针对目前飞机恒速恒频交流(CSCF-AC)电源系统故障诊断方法中存在的虚警率高、信息不能共享等问题,提出一种基于动态故障树(DFT)的CSCF-AC电源系统网络化故障诊断方法。通过在普通布尔算子中引入BEFORE算子,对CSCF-AC电源系统动态故障树进行定性分析,得到系统的失效模式,在此基础上建立一个包括机内自检、地面诊断和远程专家诊断在内具有三层结构的网络化故障诊断系统。实验结果表明,该方法有效可靠,具有一定的工程实用性。

基于改进DFTA的安全苛求系统可靠性分析

针对传统可靠性分析方法容易忽视冗余多态性、可修复性等安全苛求系统特性问题,在形式化的系统可靠性建模中引入可修复因子,提出一种模块化动态故障树分析方法。在动态和静态相结合的模块化定量分析过程中,利用马尔可夫模型和顶事件发生概率逼近算法,有效避免动态故障树分析过程中的状态组合爆炸问题,提高安全苛求系统可靠性分析的可行性和实践效率。