Diagnosability of Discrete Event Systems with Modular Structure

The diagnosis of unobservable faults in large and complex discrete event systems modeled by parallel composition of automata is considered. A modular approach is developed for diagnosing such systems. The notion of modular diagnosability is introduced and the corresponding necessary and sufficient conditions to ensure it are presented. The verification of modular diagnosability is performed by a new algorithm that incrementally exploits the modular structure of the system to save on computational effort. The correctness of the algorithm is proved. Online diagnosis of modularly diagnosable systems is achieved using only local diagnosers.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45105/1/10626_2006_Article_6177.pd

On monitoring and diagnosing classes of discrete event systems.

This thesis addresses three detection and diagnosis problems for systems with event-driven dynamics. First, the diagnosis of intermittent faults in discrete event dynamic systems is considered. A modeling methodology for discrete event systems with intermittent faults is proposed. New notions of diagnosability that provide information about the status of intermittent faults at different levels of detail are introduced. Necessary and sufficient conditions for a system to be diagnosable under each notion are specified and proven. These conditions are based upon the known technique of diagnosers, with appropriate enhancements to capture the dynamic nature of faults in the system model. Secondly, this thesis studies the diagnosis of unobservable faults in large and complex discrete event systems modeled by parallel composition of automata. A modular approach is developed to mitigate the computational difficulties in diagnosing such systems. The notion of modular diagnosability is introduced and conditions that are necessary and sufficient to ensure it are identified. For verification purposes, a new algorithm that incrementally exploits the modular structure of the system to save on computational effort is designed. The correctness of the algorithm is proven. Online diagnosis of modularly diagnosable systems is achieved using only local diagnosers. Finally, this thesis addresses the longstanding and difficult problem of detecting and classifying spatially distributed network anomalies from multiple monitoring sites on the Internet. An event-driven hierarchical framework, based on multi-criteria decision making methodologies, is developed to detect anomalous behavior in large and distributed networks. The associated tool, which implements this framework, generates temporally and spatially-correlated risk indices for each anomaly with respect to certain types of network attacks (e.g., Worm and Denial-of-Service). The main goal of this approach is to help security experts to make fast decisions when anomalies occur in large numbers and with different intensities, which is often the case in the Internet.Ph.D.Applied SciencesElectrical engineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/125050/2/3186603.pd

Diagnosis of Intermittent Faults

The diagnosis of “intermittent” faults in dynamic systems modeled as discrete event systems is considered. In many systems, faulty behavior often occurs intermittently, with fault events followed by corresponding “reset” events for these faults, followed by new occurrences of fault events, and so forth. Since these events are usually unobservable, it is necessary to develop diagnostic methodologies for intermittent faults. Prior methodologies for detection and isolation of permanent faults are no longer adequate in the context of intermittent faults, since they do not account explicitly for the dynamic behavior of these faults. This paper addresses this issue by: (i) proposing a modeling methodology for discrete event systems with intermittent faults; (ii) introducing new notions of diagnosability associated with fault and reset events; and (iii) developing necessary and sufficient conditions, in terms of the system model and the set of observable events, for these notions of diagnosability. The definitions of diagnosability are complementary and capture desired objectives regarding the detection and identification of faults, resets, and the current system status (namely, is the fault present or absent). The associated necessary and sufficient conditions are based upon the technique of “diagnosers” introduced in earlier work, albeit the structure of the diagnosers needs to be enhanced to capture the dynamic nature of faults in the system model. The diagnosability conditions are verifiable in polynomial time in the number of states of the diagnosers.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45084/1/10626_2004_Article_5266215.pd

Resolving the evolution of extant and extinct ruminants with high-throughput phylogenomics

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