Time Disturbances and Filtering of Sensors Signals in Tolerant Multi-product Job-shops with Time Constraints

Abstract

This paper deals with supervision in critical time manufacturing jobshops without assembling tasks. Such systems have a robustness property to deal with time disturbances. A filtering mechanism of sensors signals integrating the robustness values is proposed. It provides the avoidance of control freezing if the time disturbance is in the robustness intervals. This constitutes an enhancement of the filtering mechanism since it makes it possible to continue the production in a degraded mode providing the guarantees of quality and safety. When a symptom of abnormal functioning is claimed by the filtering mechanism, it is imperative to localize the time disturbance occurrence. Based upon controlled P-time Petri nets as a modeling tool, a series of lemmas are quoted in order to build a theory dealing with the localization problem.

Fuzzy Filtering of Sensors Signals in Manufacturing Systems with Time Constraints

“…The measures of the two CAO using specific values of sensor signal and Start-Event are summarized in table 5. Table 5, shows the measures obtained by using defuzzification method mentioned above.Analysing the data, it is noted that the first and the third cases represent a classic filtering mechanism of sensors signals, integrating the robustness values described in [3]. The second case, using fuzzy filtering approach, gives better results than the two cases previously analysed.…”

“…These reasons bring us to use fuzzy logic which is based on an approximate reasoning able to take into account the uncertainty and the inaccuracy of knowledge. This paper is an extension of Jerbi work [3]. In [3] was proposed an integration of the robustness in the filtering mechanism of sensors signals.…”

“…This paper is an extension of Jerbi work [3]. In [3] was proposed an integration of the robustness in the filtering mechanism of sensors signals. This mechanism, presented by Toguyeni in 1992, aims at generating symptoms for the diagnosis [4].…”

“…Therefore, the robustness intervals must be 364 A. Mhalla, N. Jerbi, S. C. Dutilleul, E. Craye, M. Benrejeb integrated in the filtering mechanism of sensors signals. In [3], a filtering mechanism of sensors signals integrating the robustness values is proposed. This mechanism allows the integration of the robustness results in the symptoms generation and the classification of the various actions reports.…”

“…It makes it possible to continue the production in a degraded mode. However the assumptions formulated in [3] are very restrictive. It is natural to consider different scenarios where the temporal specifications of the…”