Stochastic DES Fault Diagnosis with Coloured Interpreted Petri Nets

[EN] This proposal presents an online method to detect and isolate faults in stochastic discrete event systems without previous model. A coloured timed interpreted Petri Net generates the normal behavior language after an identification stage.The next step is fault detection that is carried out by comparing the observed event sequences with the expected event sequences. Once a new fault is detected, a learning algorithm changes the structure of the diagnoser, so it is able to learn new fault languages. Moreover, the diagnoser includes timed events to represent and diagnose stochastic languages. Finally, this paper proposes a detectability condition for stochastic DES and the sufficient and necessary conditions are proved.This work was supported by a grant from the Universidad del Cauca, Reference 2.3-31.2/05 2011.Muñoz-Añasco, DM.; Correcher Salvador, A.; García Moreno, E.; Morant Anglada, FJ. (2015). Stochastic DES Fault Diagnosis with Coloured Interpreted Petri Nets. 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Event-triggered Learning

The efficient exchange of information is an essential aspect of intelligent collective behavior. Event-triggered control and estimation achieve some efficiency by replacing continuous data exchange between agents with intermittent, or event-triggered communication. Typically, model-based predictions are used at times of no data transmission, and updates are sent only when the prediction error grows too large. The effectiveness in reducing communication thus strongly depends on the quality of the prediction model. In this article, we propose event-triggered learning as a novel concept to reduce communication even further and to also adapt to changing dynamics. By monitoring the actual communication rate and comparing it to the one that is induced by the model, we detect a mismatch between model and reality and trigger model learning when needed. Specifically, for linear Gaussian dynamics, we derive different classes of learning triggers solely based on a statistical analysis of inter-communication times and formally prove their effectiveness with the aid of concentration inequalities