Codiagnosability of networked discrete event systems with timing structure

Detalhes bibliográficos
Ano de defesa: 2018
Autor(a) principal: Viana, Gustavo da Silva
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Federal do Rio de Janeiro
Brasil
Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia
Programa de Pós-Graduação em Engenharia Elétrica
UFRJ
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://hdl.handle.net/11422/9361
Resumo: We address, in this work, the problem of codiagnosability of networked discrete event systems with timing structure (NDESWTS) subject to delays and loss of observations of events between the measurement sites (MS) and local diagnosers (LD). To this end, we first introduce a new timed model that represents the dynamic behavior of the plant based on the, a priori, knowledge of the minimal firing time for each transition of the plant and on the maximal delays in the communication channels that connect MS and LD. We then convert this timed model into an equivalent untimed one, and add possible intermittent packet loss in the communication network. Based on this untimed model, we present necessary and sufficient conditions for NDESWTS codiagnosability and propose two tests for its verification: one that deploys diagnosers and another one that uses verifiers. Another topic addressed in this work is the computation of τ -codiagnosability (maximal time to diagnose a failure occurrence) and K-codiagnosability (maximal number of event occurrences necessary to diagnose a failure). To this end, we propose two tests: (i) one test based on a diagnoser-like that does not require usual assumptions on language liveness and nonexistence of unobservable cycles and (ii) another one based on the extended verifier that shows not only the ambiguous paths but also those paths that lead to language diagnosis.