Event Counting of Partially-Observed Discrete-Event Systems with Uniformly and Nonuniformly Bounded Diagnosis Delays

We present an approach dealing with repeated fault events in the framework of model-based monitoring of discrete-event systems (DES). Various notions of diagnosability reported in the literature deal with uniformly bounded finite detection of counting delays over all faulty behaviors (uniform delays for brevity). The situation where the diagnosability notion of interest fails to hold under a given observation configuration leads typically to the deployment of more observational devices (e.g., sensors), which may be costly or infeasible. As an alternative to the additional deployment of observational devices, one might want to relax the uniformity of delays, while delays remain finite. To this end, we introduce a notion of diagnosability characterized with nonuniformly bounded finite counting delays (nonuniform counting delays for brevity), where finite delay bounds can vary on faulty behaviors. To evaluate the introduced notion of diagnosability with nonuniform counting delays, a polynomial-time verification algorithm is developed. Notably, the developed verification technique can readily be modified to construct a computationally superior verification algorithm for diagnosability under uniformly bounded finite counting delays (uniform counting delays for brevity) as compared to an algorithm previously reported in the literature. We also develop a novel on-line event counting algorithm that improves the time and space complexities of the currently available algorithms for the counting of special events.

Humberto E. Garcia (Corresponding author)Email:

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Tae-Sic Yoo   received the B. Eng degree from Korea University, Seoul, Korea, in 1994, the M. Eng. and the Ph.D. degree from the University of Michigan, Ann Arbor, in 1999 and 2002, respectively, all in electrical engineering. Since 2002, he has been with Argonne National Laboratory-West and Idaho National Laboratory as a researcher. He was a recipient of the distinguished graduate student awards from the University of Michigan in 2003. His general research interests are in systems and control: theory and applications. His research experience includes discrete-event systems, sensor networks, empirical data-driven systems, stochastic systems, and modeling and analysis of nuclear engineering systems. Humberto E. Garcia   Humberto E. Garcia received an Ingeniero Electricista degree from the Universidad de Carabobo, Venezuela, and MS and Ph.D. degrees in Electrical and Computer Engineering (with a minor in Mechanical Engineering) from the Pennsylvania State University, USA. He is currently with Idaho National Laboratory, being previously with Argonne National Laboratory. Dr. Garcia has over sixteen years of work experience in modeling, monitoring, control, and optimization of complex dynamical systems gained from numerous research, development, and demonstration efforts. His interests include sensor networks/systems, online condition monitoring, diagnostics, and prognostics, process monitoring and event detection, supervisory control, life-extended control, anomaly tolerant/reconfigurable systems, advanced safeguards/nonproliferation, proliferation detection, and counter-proliferation, process-infrastructure analysis, computational intelligence, and decision theory applications. His current duties include group lead, Sensor and Decision Systems, and principal investigator in several projects for advanced energy systems and national security applications. Developed technologies have been successfully demonstrated not only on simulated, hardware-in-the-loop, and lab-scale experimental test beds, but also on actual engineering-scale systems. Dr. Garcia has served as chair, panel member, and technical lead in numerous technical meetings, including being an expert member to International Atomic Energy Agency (IAEA) consultancy meetings on the subject of online condition monitoring. He has over 60 technical publications and two U.S. patents.