(t, k)-diagnosable system: A generalization of the PMC models

ln this paper, we introduce a new model for diagnosable systems called (t, k)-diagnosable system which guarantees that at least k faulty units (processors) in a system are detected provided that the number of faulty units does not exceed t. This system includes classical one-step diagnosable systems and sequentially diagnosable systems. We prove a necessary and sufficient condition for (t, k)-diagnosable system, and discuss a lower bound for diagnosability. Finally, we deal with a relation between (t, k)-diagnosability and diagnosability of classical basic models

Distributed synchronous diagnosis of discrete-event systems

Recently, the centralized and decentralized synchronous diagnosis of discreteevent systems have been proposed in the literature. In this work, we propose a di erent synchronous diagnosis strategy called distributed synchronous diagnosis. In this scheme, local diagnosers are computed based on the observation of the fault-free behavior models of the system components. It is considered that these local diagnosers are separated into networks, and are capable of communicating the occurrence of events and their current state estimate to other local diagnosers that belong to the same network. The diagnosers are implemented considering an speci c communication protocol that re nes the state estimate of the faultfree behavior of the system modules, reducing, therefore, the augmented fault-free language considered for synchronous diagnosis. In order to do so, boolean conditions are added to the transitions of the fault-free component models, which check if the occurrence of an observable event is possible according to the current state estimate of other local diagnosers. This leads to the notion of distributed synchronous diagnosability. An algorithm to verify the distributed synchronous diagnosability with polynomial complexity in the state-space of the system component models is proposed.Recentemente, o diagnóstico síncrono centralizado e descentralizado de sistemas a eventos discretos foi proposto na literatura. Neste trabalho, propomos uma estratégia de diagnóstico síncrono diferente, denominada diagnóstico síncrono distribuído. Neste esquema, diagnosticadores locais são construídos com base na observação do comportamento livre de falha dos componentes do sistema. Considera-se que esses diagnosticadores locais são agrupados em redes de comunicação e capazes de informar a ocorrência de eventos e sua estimativa de estado atual a outros diagnosticadores locais pertencentes à mesma rede. Os diagnosticadores são implementados considerando um protocolo de comunicação específico, o qual refina a estimativa de estado do comportamento livre de falha dos módulos do sistema, reduzindo, portanto, a linguagem aumentada livre de falha considerada no diagnóstico síncrono. Isso é feito com a adição de condições booleanas para a transposição de transições dos modelos livre de falha dos componentes do sistema, as quais verificam se a ocorrência de um evento observável é possível de acordo com a estimativa do estado atual dos outros diagnosticadores locais. Isso leva à noção de diagnosticabilidade síncrona distribuída. Um algoritmo para verificar a diagnosticabilidade síncrona distribuída com complexidade polinomial no espaço de estados dos modelos dos componentes do sistema é proposto

Minimal Diagnosis and Diagnosability of Discrete-Event Systems Modeled by Automata

In the last several decades, the model-based diagnosis of discrete-event systems (DESs) has increasingly become an active research topic in both control engineering and artificial intelligence. However, in contrast with the widely applied minimal diagnosis of static systems, in most approaches to the diagnosis of DESs, all possible candidate diagnoses are computed, including nonminimal candidates, which may cause intractable complexity when the number of nonminimal diagnoses is very large. According to the principle of parsimony and the principle of joint-probability distribution, generally, the minimal diagnosis of DESs is preferable to a nonminimal diagnosis. To generate more likely diagnoses, the notion of the minimal diagnosis of DESs is presented, which is supported by a minimal diagnoser for the generation of minimal diagnoses. Moreover, to either strongly or weakly decide whether a minimal set of faulty events has definitely occurred or not, two notions of minimal diagnosability are proposed. Necessary and sufficient conditions for determining the minimal diagnosability of DESs are proven. The relationships between the two types of minimal diagnosability and the classical diagnosability are analysed in depth

On-line diagnosis of sequential systems, 2

The theory and techniques applicable to the on-line diagnosis of sequential systems, were investigated. A complete model for the study of on-line diagnosis is developed. First an appropriate class of system models is formulated which can serve as a basis for a theoretical study of on-line diagnosis. Then notions of realization, fault, fault-tolerance and diagnosability are formalized which have meaningful interpretations in the the context of on-line diagnosis. The diagnosis of systems which are structurally decomposed and are represented as a network of smaller systems is studied. The fault set considered is the set of faults which only affect one component system is the network. A characterization of those networks which can be diagnosed using a purely combinational detector is achieved. A technique is given which can be used to realize any network by a network which is diagnosable in the above sense. Limits are found on the amount of redundancy involved in any such technique

Intermittent/transient fault phenomena in digital systems

An overview of the intermittent/transient (IT) fault study is presented. An interval survivability evaluation of digital systems for IT faults is discussed along with a method for detecting and diagnosing IT faults in digital systems

Discrete and hybrid methods for the diagnosis of distributed systems

Many important activities of modern society rely on the proper functioning of complex systems such as electricity networks, telecommunication networks, manufacturing plants and aircrafts. The supervision of such systems must include strong diagnosis capability to be able to effectively detect the occurrence of faults and ensure appropriate corrective measures can be taken in order to recover from the faults or prevent total failure. This thesis addresses issues in the diagnosis of large complex systems. Such systems are usually distributed in nature, i.e. they consist of many interconnected components each having their own local behaviour. These components interact together to produce an emergent global behaviour that is complex. As those systems increase in complexity and size, their diagnosis becomes increasingly challenging. In the first part of this thesis, a method is proposed for diagnosis on distributed systems that avoids a monolithic global computation. The method, based on converting the graph of the system into a junction tree, takes into account the topology of the system in choosing how to merge local diagnoses on the components while still obtaining a globally consistent result. The method is shown to work well for systems with tree or near-tree structures. This method is further extended to handle systems with high clustering by selectively ignoring some connections that would still allow an accurate diagnosis to be obtained. A hybrid system approach is explored in the second part of the thesis, where continuous dynamics information on the system is also retained to help better isolate or identify faults. A hybrid system framework is presented that models both continuous dynamics and discrete evolution in dynamical systems, based on detecting changes in the fundamental governing dynamics of the system rather than on residual estimation. This makes it possible to handle systems that might not be well characterised and where parameter drift is present. The discrete aspect of the hybrid system model is used to derive diagnosability conditions using indicator functions for the detection and isolation of multiple, arbitrary sequential or simultaneous events in hybrid dynamical networks. Issues with diagnosis in the presence of uncertainty in measurements due sensor or actuator noise are addressed. Faults may generate symptoms that are in the same order of magnitude as the latter. The use of statistical techniques,within a hybrid system framework, is proposed to detect these elusive fault symptoms and translate this information into probabilities for the actual operational mode and possibility of transition between modes which makes it possible to apply probabilistic analysis on the system to handle the underlying uncertainty present

An introduction of small-scale intelligent manufacturing system

Embargoed OA, manuscript version after 24 months from publishing date. Link to publishers version: http://doi.org/10.1109/SIMS.2016.7802896Manufacturing companies in Northern Peripheral and Arctic region are predominately small and medium-sized and face considerable challenges like geographical isolation and a lack of benefits offered by industrial clusters. For the ultimate goal of enhancing their competitiveness in a global market, it is imperative for companies to innovate or adopt innovations in order to quickly response to changes in market, meet customer demands, reduce time-to-market and lower cost. A novel concept for small-scale intelligent manufacturing systems (SIMS) is introduced, in which diverse methods and innovative technologies can be applied and integrated. This paper gives an introduction of SIMS, defines its design objectives, and summarizes major relevant tools, techniques and paradigms for the development of SIMS, to generate a facilitative environment for small and medium-scale manufacturing enterprises to embrace new and innovative technologies

Theory and design of reliable spacecraft data systems

Theory and techniques applicable to design, analysis, and fault diagnosis of reliable spacecraft data system

SUPERVISORY CONTROL AND FAILURE DIAGNOSIS OF DISCRETE EVENT SYSTEMS: A TEMPORAL LOGIC APPROACH

Discrete event systems (DESs) are systems which involve quantities that take a discrete set of values, called states, and which evolve according to the occurrence of certain discrete qualitative changes, called events. Examples of DESs include many man-made systems such as computer and communication networks, robotics and manufacturing systems, computer programs, and automated trac systems. Supervisory control and failure diagnosis are two important problems in the study of DESs. This dissertation presents a temporal logic approach to the control and failure diagnosis of DESs. For the control of DESs, full branching time temporal logic-CTL* is used to express control specifications. Control problem of DES in the temporal logic setting is formulated; and the controllability of DES is defined. By encoding the system with a CTL formula, the control problem of CTL* is reduced to the decision problem of CTL*. It is further shown that the control problem of CTL* (resp., CTL{computation tree logic) is complete for deterministic double (resp., single) exponential time. A sound and complete supervisor synthesis algorithm for the control of CTL* is provided. Special cases of the control of computation tree logic (CTL) and linear-time temporal logic (LTL) are also studied; and for which algorithms of better complexity are provided. For the failure diagnosis of DESs, LTL is used to express fault specifications. Failure diagnosis problem of DES in the temporal logic setting is formulated; and the diagnosability of DES is defined. The problem of testing the diagnosability is reduced to that of model checking. An algorithm for the test of diagnosability and the synthesis of a diagnoser is obtained. The algorithm has a polynomial complexity in the number of system states and the number of fault specifications. For the diagnosis of repeated failures in DESs, different notions of repeated failure diagnosability, K-diagnosability, [1,K]-diagnosability, and [1,1]-diagnosability, are introduced. Polynomial algorithms for checking these various notions of repeated failure diagnosability are given, and a procedure of polynomial complexity for the on-line diagnosis of repeated failures is also presented