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— We investigate diagnosability of stochastic discrete-event systems where the observation of certain events is unreliable, that is, there are non-zero probabilities of the misdetection and misclassification of events based on faulty sensor readings. Such sensor unreliability is unavoidable in applications such as nuclear energy generation. We propose the(More)
We introduce the notion of repeated failure diagnosability for diagnosing the occurrence of a repeated number of failures in discrete event systems. This generalizes the earlier notion of diagnosability that was used to diagnose the occurrence of a failure, but from which the information regarding the multiplicity of the occurrence of the failure could not(More)
AbstructThis paper presents a reconfigurable approach to implement decision and control systems for complex dynamic processes. The proposed supervisory control system is a reconfigurable hybrid architecture structured into three functional levels of hierarchy, namely, execution, supervision, and coordination. While the bottom execution level is constituted(More)
A theoretical framework and its practical implications for formulating and implementing model-based monitoring of discrete flow networks are discussed. Possible flows of items are described as discrete-event (DE) traces. Each trace defines the DE sequence(s) that are triggered when an entity follows a given flow-path, visiting tracking locations within the(More)
For discrete event systems under partial observation, we study the problem of selection of an optimal set of sensors that can provide sufficient yet minimal events observation information needed to accomplish the task at hand such as that of control or estimation. The sufficiency of the observed information is captured as the fulfillment of a desired formal(More)