Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning pr...Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.展开更多
This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distr...This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.展开更多
Reasoning theories are divided into certainty reasoning theories and uncertainty reasoning theories. Now, only certainty reasoning theories are used to detect and diagnose satellite faults. However, in practice, it is...Reasoning theories are divided into certainty reasoning theories and uncertainty reasoning theories. Now, only certainty reasoning theories are used to detect and diagnose satellite faults. However, in practice, it is difficult to detect and diagnose some faults of the satellite automatically only by use of certainty reasoning theories. The reason is that detection and diagnosis of these faults require a rational reasoning and a fault tolerant capability. Fortunately, uncertainty reasoning theories can meet these requirements. It is attracting attention of many experts in the space field all over the world that uncertainty reasoning theories are applied to detect and diagnose satellite faults. Uncertainty reasoning theories include several kinds of theories, such as inclusion degree theory, rough set theory, evidence reasoning theory, probabilistic reasoning theory, fuzzy reasoning theory, and so on. Inclusion degree theory, rough set theory and evidence reasoning theory are three advanced ones. Based on these three theories respectively, the author introduces three new methods to detect and diagnose satellite faults in this paper. It is shown that the methods, suitable for detecting and diagnosing satellite faults, especially uncertainty faults, can remedy the defects of the current methods.展开更多
Diagnosis and prediction of satellite fault are more difficult than that of other equipment due to the complex structure of satellites and the presence of multi excite sources of satellite faults. Generally, one kind ...Diagnosis and prediction of satellite fault are more difficult than that of other equipment due to the complex structure of satellites and the presence of multi excite sources of satellite faults. Generally, one kind of reasoning model can only diagnose and predict one kind of satellite faults. In this paper the author introduces an application of a new method using multi modal reasoning to diagnose and predict satellite faults. The method has been used in the development of knowledge based satellite fault diagnosis and recovery system (KSFDRS) successfully. It is shown that the method is effective.展开更多
The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become...The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints.So far,little research has been carried out in this field.This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes.Three optimization objectives are considered simultaneously: maximum probability of average fault,maximum average importance,and minimum average complexity of test.Under the constraints of both known symptoms and the causal relationship among different components,a multi-objective optimization mathematical model is set up,taking minimizing cost of fault reasoning as the target function.Since the problem is non-deterministic polynomial-hard(NP-hard),a modified multi-objective ant colony algorithm is proposed,in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives.At last,a Pareto optimal set is acquired.Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set,through which the final fault causes can be identified according to decision-making demands,thus realize fault reasoning of the multi-constraint and multi-objective complex system.Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model,which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.展开更多
换流阀是直流输电工程的核心设备,其价值约占换流站成套设备总价的22%~25%,其运行状态直接影响直流输电系统的可靠性。文章针对换流站阀区现有故障人工定位效率低、耗时长、严重依赖运行人员水平等问题,提出了基于度量学习和知识推理的...换流阀是直流输电工程的核心设备,其价值约占换流站成套设备总价的22%~25%,其运行状态直接影响直流输电系统的可靠性。文章针对换流站阀区现有故障人工定位效率低、耗时长、严重依赖运行人员水平等问题,提出了基于度量学习和知识推理的换流站阀区故障定位方法。文中基于PSCAD(power system computer aided design)仿真分析软件构建了阀区故障仿真模型,生成阀区典型故障录波数据,通过时频域变换获取前100根最大谱线;构建基于度量学习的数据降维网络,通过最大化类间距离、最小化类内距离实现录波电气量特征提取;形成电气量-开关量-故障类型三元组,以知识图谱形式存储故障定位知识,设计知识图谱推理算法实现基于录波数据分析的故障定位。实验结果表明,该方法基于换流站阀区录波数据实现故障定位检出率在92%以上,将有效提升运行人员故障定位效率。展开更多
A combined logic- and model-based approach to fault detection and identification （FDI） in a suction foot control system of a wall-climbing robot is presented in this paper. For the control system, some fault models ...A combined logic- and model-based approach to fault detection and identification （FDI） in a suction foot control system of a wall-climbing robot is presented in this paper. For the control system, some fault models are derived by kinematics analysis. Moreover, the logic relations of the system states are known in advance. First, a fault tree is used to analyze the system by evaluating the basic events （elementary causes）, which can lead to a root event （a particular fault）. Then, a multiple-model adaptive estimation algorithm is used to detect and identify the model-known faults. Finally, based on the system states of the robot and the results of the estimation, the model-unknown faults are also identified using logical reasoning. Experiments show that the proposed approach based on the combination of logical reasoning and model estimating is efficient in the FDI of the robot.展开更多
离心式压缩机是石油化工行业天然气管网的关键设备,其高故障率对所属企业会造成较大的经济损失。提出了一种基于本体的离心式压缩机故障诊断方法。首先,以石油化工企业积累的离心式压缩机故障分析报告为知识源,通过本体建模从知识源中...离心式压缩机是石油化工行业天然气管网的关键设备,其高故障率对所属企业会造成较大的经济损失。提出了一种基于本体的离心式压缩机故障诊断方法。首先,以石油化工企业积累的离心式压缩机故障分析报告为知识源,通过本体建模从知识源中提取故障诊断知识,促进故障诊断知识的集成、共享和重用;然后,使用本体软件Protégé构建故障诊断知识库,通过语义Web规则语言SWRL(Semantic Web Rule Language)实现基于规则的推理(Rule-Based Reasoning,RBR),并通过软件Neo4j进行知识存储和查询。采用该故障诊断方法,对离心式压缩机组合成油系统进行了测试。结果表明,该故障诊断方法有效,可提升故障知识的应用效率,并为离心式压缩机诊断决策提供优质的知识基础。展开更多
基金Supported by the National Natural Science Foundation of China(61374137,61490701,61174119)the State Key Laboratory of Integrated Automation of Process Industry Technology and Research Center of National Metallurgical Automation Fundamental Research Funds(2013ZCX02-03)
文摘Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.
文摘This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.
文摘Reasoning theories are divided into certainty reasoning theories and uncertainty reasoning theories. Now, only certainty reasoning theories are used to detect and diagnose satellite faults. However, in practice, it is difficult to detect and diagnose some faults of the satellite automatically only by use of certainty reasoning theories. The reason is that detection and diagnosis of these faults require a rational reasoning and a fault tolerant capability. Fortunately, uncertainty reasoning theories can meet these requirements. It is attracting attention of many experts in the space field all over the world that uncertainty reasoning theories are applied to detect and diagnose satellite faults. Uncertainty reasoning theories include several kinds of theories, such as inclusion degree theory, rough set theory, evidence reasoning theory, probabilistic reasoning theory, fuzzy reasoning theory, and so on. Inclusion degree theory, rough set theory and evidence reasoning theory are three advanced ones. Based on these three theories respectively, the author introduces three new methods to detect and diagnose satellite faults in this paper. It is shown that the methods, suitable for detecting and diagnosing satellite faults, especially uncertainty faults, can remedy the defects of the current methods.
文摘Diagnosis and prediction of satellite fault are more difficult than that of other equipment due to the complex structure of satellites and the presence of multi excite sources of satellite faults. Generally, one kind of reasoning model can only diagnose and predict one kind of satellite faults. In this paper the author introduces an application of a new method using multi modal reasoning to diagnose and predict satellite faults. The method has been used in the development of knowledge based satellite fault diagnosis and recovery system (KSFDRS) successfully. It is shown that the method is effective.
基金supported by Sub-project of Key National Science and Technology Special Project of China(Grant No.2011ZX05056)
文摘The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints.So far,little research has been carried out in this field.This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes.Three optimization objectives are considered simultaneously: maximum probability of average fault,maximum average importance,and minimum average complexity of test.Under the constraints of both known symptoms and the causal relationship among different components,a multi-objective optimization mathematical model is set up,taking minimizing cost of fault reasoning as the target function.Since the problem is non-deterministic polynomial-hard(NP-hard),a modified multi-objective ant colony algorithm is proposed,in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives.At last,a Pareto optimal set is acquired.Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set,through which the final fault causes can be identified according to decision-making demands,thus realize fault reasoning of the multi-constraint and multi-objective complex system.Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model,which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.
文摘换流阀是直流输电工程的核心设备,其价值约占换流站成套设备总价的22%~25%,其运行状态直接影响直流输电系统的可靠性。文章针对换流站阀区现有故障人工定位效率低、耗时长、严重依赖运行人员水平等问题,提出了基于度量学习和知识推理的换流站阀区故障定位方法。文中基于PSCAD(power system computer aided design)仿真分析软件构建了阀区故障仿真模型,生成阀区典型故障录波数据,通过时频域变换获取前100根最大谱线;构建基于度量学习的数据降维网络,通过最大化类间距离、最小化类内距离实现录波电气量特征提取;形成电气量-开关量-故障类型三元组,以知识图谱形式存储故障定位知识,设计知识图谱推理算法实现基于录波数据分析的故障定位。实验结果表明,该方法基于换流站阀区录波数据实现故障定位检出率在92%以上,将有效提升运行人员故障定位效率。
基金supported by the Hi-tech Research and Development Program of China (No.2006AA420203)
文摘A combined logic- and model-based approach to fault detection and identification （FDI） in a suction foot control system of a wall-climbing robot is presented in this paper. For the control system, some fault models are derived by kinematics analysis. Moreover, the logic relations of the system states are known in advance. First, a fault tree is used to analyze the system by evaluating the basic events （elementary causes）, which can lead to a root event （a particular fault）. Then, a multiple-model adaptive estimation algorithm is used to detect and identify the model-known faults. Finally, based on the system states of the robot and the results of the estimation, the model-unknown faults are also identified using logical reasoning. Experiments show that the proposed approach based on the combination of logical reasoning and model estimating is efficient in the FDI of the robot.
文摘离心式压缩机是石油化工行业天然气管网的关键设备,其高故障率对所属企业会造成较大的经济损失。提出了一种基于本体的离心式压缩机故障诊断方法。首先,以石油化工企业积累的离心式压缩机故障分析报告为知识源,通过本体建模从知识源中提取故障诊断知识,促进故障诊断知识的集成、共享和重用;然后,使用本体软件Protégé构建故障诊断知识库,通过语义Web规则语言SWRL(Semantic Web Rule Language)实现基于规则的推理(Rule-Based Reasoning,RBR),并通过软件Neo4j进行知识存储和查询。采用该故障诊断方法,对离心式压缩机组合成油系统进行了测试。结果表明,该故障诊断方法有效,可提升故障知识的应用效率,并为离心式压缩机诊断决策提供优质的知识基础。
