Robust Disjunctive-Codiagnosability of Discrete-Event Systems Against Permanent Loss of Observations

K-diagnosability analysis of bounded and unbounded Petri nets using linear optimization

“…In fact, it is worth noting that substantial improvements in terms of complexity have been brought by diagnosability techniques, that consider an FSA setting. In recent years, polynomial time algorithms w.r.t the number of states of the FSA model have been proposed in [17,25]. In [27], Viana et al proposed an even more efficient (polynomial) algorithm for (co)diagnosability analysis and then -(co)diagnosability computation using a verifier model.…”

Codiagnosability of Networked Discrete Event Systems With Timing Structure

“…TOMOLA et al [61] proposed an algorithm for the computation of the delay bound for robust disjunctive decentralized diagnosis based on the algorithm for the computation of the delay bound in the non-robust case using the verifier automaton proposed in [47]. Like the strategy developed in [45], the extension to τ -diagnosability is not straightforward.…”

“…In addition, in [41] the communication delay between two local diagnosers is considered equal, and it is assumed that there is no delay between the measurement sites and diagnosers. The problem of DES subject to unreliable observations of events was addressed in [54] and [61] (in the context of failure diagnosis) without considering communication networks. In this work, we model the loss of observation based on the technique proposed by CARVALHO et al in [54] In [42,43], the definition of network codiagnosability of DES subject to event communication delays was introduced, where the concept of step [33,35] was used to measure communication delays, i.e., k ∈ N steps accounts for the occurrence of, at most, k events until the information of the event executed by the plant arrives at the local diagnoser.…”

Redução de Diagnosticadores de Sistemas a Eventos Discretos

“…Se o sistema puder ser abstraído como um Sistema a Evento Discreto (SED), então a falha pode ser modelada como um evento que deve ser identificado pelo sistema de diagnóstico. Neste caso, o atraso para o diagnóstico, definido como o maior número de ocorrências de eventos que o sistema pode executar após a falha ocorrer até sua detecção (Yoo and Garcia, 2003;Tomola et al, 2017;Viana et al, 2019), pode ser utilizado para determinar a eficiência do método de diagnóstico.…”