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,f...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system reliability analysis method,fault tree analysis(FTA)method,based on static logic and static failure mechanism is no longer applicable for dynamic systems reliability analysis.Dynamic fault tree(DFT)analysis method can solve this problem effectively.In this method,DFT first should be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.展开更多
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 neuro...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.展开更多
提出了基于地理信息系统(geographic information system,GIS)的大规模配电网可靠性评估方法。为简化数据存储方式及提高搜索速度,提出了配电网设备分类方法及树形编码方式,利用GIS中设备的电房属性实现配电网拓扑结构搜索,利用设备的...提出了基于地理信息系统(geographic information system,GIS)的大规模配电网可靠性评估方法。为简化数据存储方式及提高搜索速度,提出了配电网设备分类方法及树形编码方式,利用GIS中设备的电房属性实现配电网拓扑结构搜索,利用设备的树形编码判断设备的上下游关系,并据此提出了转供判断方法,最后在可靠性计算中还考虑了预安排停电的影响。算例结果验证了该方法的有效性。展开更多
文摘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.
文摘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.
文摘安全隐患、未遂事故等异常事件是小事故升级为重大事故的早期预警,可用来建立事故模型识别源头事件及纠正保护系统中的不安全因素。结合液化天然气(LNG)库区的工艺特点和事故特征,对系统危害辨识、预测及预防(system hazard identification,prediction and prevention,SHIPP)模型改进,提出一种将故障树、贝叶斯网络与A-star算法融合的风险评估建模方法。首先依托专家经验,结合事故报警数据库中的异常事件建立安全屏障模型和故障树;然后遵循链式法则将故障树映射为贝叶斯网络;最后与改进的A-star算法融合确定事故发生途径。基于LNG事故报警数据库的研究表明,该方法相较于传统的SHIPP模型,可以实现动态前向风险评估并量化事故之间的条件概率,反向模拟安全屏障失效时的事故发生过程。研究成果可为LNG库区的系统安全、风险规避提供合理设计及决策。
基金Project supported by the Major Program from the Ministry of Science and Technology of China:High speed compound CNC machine tool and key technological innovation platform(Grant No.2011ZX04016-21)
文摘提出了基于地理信息系统(geographic information system,GIS)的大规模配电网可靠性评估方法。为简化数据存储方式及提高搜索速度,提出了配电网设备分类方法及树形编码方式,利用GIS中设备的电房属性实现配电网拓扑结构搜索,利用设备的树形编码判断设备的上下游关系,并据此提出了转供判断方法,最后在可靠性计算中还考虑了预安排停电的影响。算例结果验证了该方法的有效性。
