Optimal control of discrete event systems
Optimal control of discrete event systems
State Observation and Diagnosis of Discrete-Event SystemsDescribed by Stochastic Automata
Discrete Event Dynamic Systems
Diagnosis of Intermittent Faults
Discrete Event Dynamic Systems
Characterizations of quantum automata
Theoretical Computer Science
Approximating the Minimal Sensor Selection for Supervisory Control
Discrete Event Dynamic Systems
Discrete Event Dynamic Systems
Fuzzy discrete-event systems under fuzzy observability and a test algorithm
IEEE Transactions on Fuzzy Systems
Diagnosability of fuzzy discrete-event systems: a fuzzy approach
IEEE Transactions on Fuzzy Systems
Sequential window diagnoser for discrete-event systems under unreliable observations
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
Modeling and control of fuzzy discrete event systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Supervisory control of fuzzy discrete event systems: a formal approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Model-based detection of routing events in discrete flow networks
Automatica (Journal of IFAC)
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Complex engineering systems have to be carefully monitored to meet demanding performance requirements, including detecting anomalies in their operations. There are two major monitoring challenges for these systems. The first challenge is that information collected from the monitored system is often partial and/or unreliable, in the sense that some occurred events may not be reported and/or may be reported incorrectly (e.g., reported as another event). The second is that anomalies often consist of sequences of event patterns separated in space and time. This paper introduces and analyzes a diagnoser algorithm that meets these challenges for detecting and counting occurrences of anomalies in engineering systems. The proposed diagnoser algorithm assumes that models are available for characterizing plant operations (via stochastic automata) and sensors (via probabilistic mappings) used for reporting partial and unreliable information. Methods for analyzing the effects of model uncertainties on the diagnoser performance are also discussed. In order to select configurations that reduce sensor costs, while satisfying diagnoser performance requirements, a sensor configuration selection algorithm developed in previous work is then extended for the proposed diagnoser algorithm. The proposed algorithms and methods are then applied to a multi-unit-operation system, which is derived from an actual facility application. Results show that the proposed diagnoser algorithm is able to detect and count occurrences of anomalies accurately and that its performance is robust to model uncertainties. Furthermore, the sensor configuration selection algorithm is able to suggest optimal sensor configurations with significantly reduced costs, while still yielding acceptable performance for counting the occurrences of anomalies.