面向多工位制造系统可诊断性的传感器布置优化

为获得足够和有效的测量信息,提高系统的可诊断性,应采用合适的测量策略并对传感器布置进行优化.为此,提出了量化描述系统可诊断性的三个测度(可检测性/可分离性/可预测性),并将多工位制造系统测量信息处理分为工位间传递和工位内检测.在保障可诊断性的前提下,提出了传感器布置优化方法,降低了测量费用和诊断时间.通过具体加工实例,验证了该方法的正确性和有效性,为传感器布置优化提供了新的理论依据和实践参考.     

分层超立方网络的可靠性评估

针对目前关于分层超立方网络的可靠性研究不够系统,严重制约了其应用和推广的问题,以分层超立方网络为研究对象,在得到分层超立方网络相关拓扑性质的基础上,通过理论推导得到了n维分层超立方(HHCn)在PMC模型和MM*模型下的h-额外条件诊断度和t/s-诊断度.此外,设计了HHCn在PMC模型和MM*模型下的t/s-诊断算法...     

离散事件系统基于模型诊断的研究进展

近年来,针对离散事件系统的基于模型诊断方法在大型通讯网络、电网故障诊断等领域获得了成功应用,成为人工智能与控制工程领域的热门研究课题。介绍了针对离散事件系统的基于模型诊断的基本思想与建模方法,从不同的角度对使用自动机建模的各种主要诊断方法进行了评析与比较;讨论了系统可诊断性判定方法的研究进展。从系统建模、分布式在线诊断、不完备模型下的诊断以及系统实现等方面,展望了针对离散事件系统的基于模型诊断领域中有待解决的问题。     

Detection,isolation and diagnosability analysis of intermittent faults in stochastic systems

Intermittent faults (IFs) have the properties of unpredictability, non-determinacy, inconsistency and repeatability, switching systems between faulty and healthy status. In this paper, the fault detection and isolation (FDI) problem of IFs in a class of linear stochastic systems is investigated. For the detection and isolation of IFs, it includes: (1) to detect all the appearing time and the disappearing time of an IF; (2) to detect each appearing (disappearing) time of the IF before the subsequent disappearing (appearing) time; (3) to determine where the IFs happen. Based on the outputs of the observers we designed, a novel set of residuals is constructed by using the sliding-time window technique, and two hypothesis tests are proposed to detect all the appearing time and disappearing time of IFs. The isolation problem of IFs is also considered. Furthermore, within a statistical framework, the definition of the diagnosability of IFs is proposed, and a sufficient condition is brought forward for the diagnosability of IFs. Quantitative performance analysis results for the false alarm rate and missing detection rate are discussed, and the influences of some key parameters of the proposed scheme on performance indices such as the false alarm rate and missing detection rate are analysed rigorously. The effectiveness of the proposed scheme is illustrated via a simulation example of an unmanned helicopter longitudinal control system.     

具有多项式时间复杂性的离散事件系统安全诊断

离散事件系统的故障诊断能将已发生的不可观故障事件及时诊断出来,但往往容易忽略故障诊断期间系统的安全性.为解决这一问题,提出了一种具有多项式时间复杂性的安全故障诊断方法.先对离散事件系统的安全可诊断性进行了形式化,再通过构造一个非法语言识别器对系统被禁止操作序列进行识别,并在此基础上构建了一个对系统实施安全诊断的安全验证器,得到了一个关于离散事件系统安全可诊断性的充分必要条件,实现了对系统的安全故障诊断.同时,通过对安全验证器的构建与安全可诊断性的判定的复杂性分析,得到了该安全故障诊断方法可在多项式时间内实现等结论.     

Diagnosability of discrete event systems subject to permanent sensor failures

This paper considers a failure diagnosis problem for discrete event systems subject to permanent sensor failures. A notion of diagnosability subject to permanent sensor failures is introduced with respect to a certain nondeterministic observation mask. For its verification, an aggregated Mealy automaton with a deterministic and state-dependent observation mask is defined. It is shown that the diagnosability of the aggregated Mealy automaton is equivalent to the diagnosability of the original system subject to permanent sensor failures. Then, a method for verifying the diagnosability of the aggregated Mealy automaton is presented. Moreover, the delay bound within which the occurrence of any failure string can be detected subject to permanent sensor failures is computed.     

A fast diagnosis algorithm for interconnection network in high performance computers

In high performance computers, a popular interconnection network, the folded hypercube (FHC), possesses smaller diameter, larger connectivity, better reliability and fault tolerance capability as compared with a hypercube counterpart. This paper addresses the fault identification of FHC multiprocessor interconncection systems under the MM^* model. The pessimistic one-step diagnosability of FHC networks is first determined. On the basis, a pessimistic one-step diagnosis algorithm tailored for FHC multiprocessor systems is proposed. The presented algorithm can isolate all faulty nodes to within a set which has at most one fault-free node, and can run in linear time.     

The pessimistic diagnosability of alternating group graphs under the PMC model

The process of identifying faulty processors is called the diagnosis of the system. Several diagnostic models have been proposed, the most popular is the PMC (Preparata, Metze and Chen) diagnostic model. The pessimistic diagnosis strategy is a classic strategy based on the PMC model in which isolates all faulty nodes within a set containing at most one fault-free node. A system is t/t$t/t$-diagnosable if, provided the number of faulty processors is bounded by t, all faulty processors can be isolated within a set of size at most t with at most one fault-free node mistaken as a faulty one. The pessimistic diagnosability of a system G  , denoted by _tp(G)$t_p(G)$, is the maximal number of faulty processors so that the system G   is t/t$t/t$-diagnosable. Jwo et al. [11] introduced the alternating group graph as an interconnection network topology for computing systems. The proposed graph has many advantages over hypercubes and star graphs. For example, for all alternating group graphs, every pair of vertices in the graph are connected by a Hamiltonian path and the graph can embed cycles with arbitrary length with dilation 1. In this article, we completely determine the pessimistic diagnosability of an n  -dimensional alternating group graph, denoted by _AGn${\mathit{AG}}_n$. Furthermore, _tp(_AGn)=4n−11$t_p({\mathit{AG}}_n)=4n-11$ for n≥4$n\ge 4$.     

Diagnosability of fuzzy discrete event systems

In this paper, discrete event systems (DESs) are reformulated as fuzzy discrete event systems (FDESs) and fuzzy discrete event dynamical systems (FDEDSs). These frameworks include fuzzy states, events and IF-THEN rules. In these frameworks, all events occur at the same time with different membership degrees. Fuzzy states and events have been introduced to describe uncertainties that occur often in practical problems, such as fault diagnosis applications. To measure a diagnoser’s fault discrimination ability, a fuzzy diagnosability degree is proposed. If the diagnosability of the degree of the system yields one a diagnoser can be implemented to identify all possible fault types related to a system. For any degree less than one, researchers should not devote their time to distinguish all possible fault types correctly. Thus, two different diagnosability definitions FDEDS and FDES are introduced. Due to the specialized fuzzy rule-base embedded in the FDEDS, it is capable of representing a class of non-linear dynamic system. Computationally speaking, the framework of diagnosability of the FDEDS is structurally similar to the framework of diagnosability of a non-linear system. The crisp DES diagnosability has been turned into the term fuzzy diagnosability for the FDES. The newly proposed diagnosability definition allows us to define a degree of diagnosability in a class of non-linear systems. In addition, a simple fuzzy diagnosability checking method is introduced and some numerical examples are provided to illustrate this theoretical development. Finally, the potential applications of the proposed method are discussed.     

面向可重组制造系统的快速诊断技术研究

在分析现有质量控制技术的基础上,提出了面向可重组制造系统故障诊断的架构和一种基于粗糙集故障诊断的方法。该方法利用传统统计过程控制数据,并结合现场传感器数据作为数据源,以粗糙集为知识获取工具进行数据处理,得到最小规则集,最终得到故障诊断结果。以一个实例验证了设计方案的有效性。