Resilient monitoring systems, considered in this paper, are sensor networks that degrade gracefully under malicious attacks on their sensors, causing them to project misleading information. The goal of this paper is to design, analyze, and evaluate the performance of a resilient monitoring system intended to monitor plant conditions (normal or anomalous). The architecture developed consists of four layers: data quality assessment, process variable assessment, plant condition assessment, and sensor network adaptation. Each of these layers is analyzed by either analytical or numerical tools. The performance of the overall system is evaluated using a simplified boiler/turbine plant. The measure of resiliency is quantified based on the Kullback-Leibler divergence and shown to be sufficiently high in all scenarios considered.

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 be obtained. It is possible that in some systems the same type of failure repeats a multiple number of times. It is desirable to have a diagnoser which not only diagnoses that such a failure has occurred, but also determines the number of times the failure has occurred. To aid such analysis we introduce the notions of K-diagnosability (K failures diagnosability), [1, K]-diagnosability (1 through K failures diagnosability), and [1, /spl infin/]-diagnosability (1 through /spl infin/ failures diagnosability). Here the first (resp., last) notion is the weakest (resp., strongest) of all three and the earlier notion of diagnosability is the same as that of K-diagnosability or that of [1, K]-diagnosability with K=1. We give polynomial algorithms for checking these various notions of repeated failure diagnosability and also present a procedure of polynomial complexity for the online diagnosis of repeated failures.

Two basic approaches can be taken to model physical systems. One approach derives a model structure from the known physical laws. However, obtaining a model with the required fidelity can be difficult if the system is not well understood. A second approach is to employ a black-box structure to learn the implicit input-output relationships from measurements in which no particular attention is paid to modeling the underlying processes. Clearly, one should exploit whatever physical insight one has into the properties of the system. This paper describes a method that draws on the respective strengths of each of these two approaches. The technique combines known first principles knowledge derived from physical modeling with measured input-output mappings derived from neural processing to produce a computer model of a dynamical process. The technique is used to diagnose operational changes of mechanical equipment by statistically comparing, using a likelihood test, the predicted model output for the given measured input with the actual process output. Experiments with a peristaltic pump were conducted and results are presented here.

New processing activities for the decommissioning of the Experimental Breeder Reactor II are being carried out at Argonne National Laboratory. The task addressed in this paper is a process to convert metallic sodium to sodium carbonate. The main idea is to characterize this sodium operation as a system that integrates real-time continuous and discrete-event components and then apply hybrid system techniques to design and implement the control and supervisory policies. This paper introduces the research in progress at ANL on this conversion process, the flow of material, and the hybrid control solution.