Tae-Sic Yoo

Diagnosability of stochastic discrete-event systems under unreliable observations

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 notions of uA- and uAA-diagnosability for stochastic automata and demonstrate their relationship with the concepts of A- and AA-diagnosabilty defined previously. We extend the concept of the stochastic diagnoser to the unreliable observation paradigm and find conditions for uA- and uAA-diagnosability.

Diagnosis of behaviors of interest in partially-observed discrete-event systems

Abstract In this paper, we address the problem of diagnosing the behaviors of interest in discrete-event systems. To this end, we introduce the notion of language-diagnosability, based on language specifications that generalizes diagnosability based on event specifications. A polynomial-time algorithm for verifying language-diagnosability is developed. Building upon the verification algorithm, we develop a polynomial-time algorithm for computing the worst case detection delay of a given system. The computation of the worst case detection delay involves the shortest path computation of a weighted, directed graph. We exploit a special weighting structure of the graph resulting from the verification algorithm, which enables an algorithm with a lower complexity than the commonly used Bellman–Ford shortest path algorithm.

A diagnoser algorithm for anomaly detection in DEDS under partial and unreliable observations: characterization and inclusion in sensor configuration optimization

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.

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.

Selecting observation platforms for optimized anomaly detectability under unreliable partial observations

Diagnosers for keeping track on the occurrences of special events in the framework of unreliable partially-observed discrete-event dynamical systems were developed in previous work. This paper considers observation platforms consisting of sensors that provide partial and unreliable observations and of diagnosers that analyze them. Diagnosers in observation platforms typically perform better as sensors providing the observations become more costly or increase in number. This paper proposes a methodology for finding an observation platform that achieves an optimal balance between cost and performance, while satisfying given observability requirements and constraints. Since this problem is generally computational hard in the framework considered, an observation platform optimization algorithm is utilized that uses two greedy heuristics, one myopic and another based on projected performances. These heuristics are sequentially executed in order to find best observation platforms. The developed algorithm is then applied to an observation platform optimization problem for a multi-unit-operation system. Results show that improved observation platforms can be found that may significantly reduce the observation platform cost but still yield acceptable performance for correctly inferring the occurrences of special events.

Sensor configuration selection for discrete-event systems under unreliable observations

Algorithms for counting the occurrences of special events in the framework of partially-observed discrete-event dynamical systems (DEDS) were developed in previous work. Their performances typically become better as the sensors providing the observations become more costly or increase in number. This paper addresses the problem of finding a sensor configuration that achieves an optimal balance between cost and the performance of the special event counting algorithm, while satisfying given observability requirements and constraints. Since this problem is generally computational hard in the framework considered, a sensor optimization algorithm is developed using two greedy heuristics, one myopic and the other based on projected performances of candidate sensors. The two heuristics are sequentially executed in order to find best sensor configurations. The developed algorithm is then applied to a sensor optimization problem for a multi-unit-operation system. Results show that improved sensor configurations can be found that may significantly reduce the sensor configuration cost but still yield acceptable performance for counting the occurrences of special events.

Erratum to Model-based detection of routing events in discrete flow networks [Automatica 41 (2005) 583-594]

Humberto E. Garcia received his electrical engineering degree from the Universidad de Carabobo, Venezuela, and his M.S. degree and Ph.D. in electrical and computer engineering, with a minor in mechanical engineering, from the Pennsylvania State University. He has held research positions with the Pennsylvania State University, the Pennsylvania Transportation Institute, and Argonne National Laboratory. He is currently a Group Leader in sensor and decision systems with the Idaho National Laboratory. He serves as a contributor, reviewer, and committee/panel member to technical meetings and organizations such as the American Nuclear Society, the Institute of Electrical and Electronic Engineers, the International Federation of Automatic Control, and the Society for Computer Simulation. He is an Associate Editor of the International Journal of Modeling and Simulation. His primary research interests include systems modeling, analysis, integration, optimization, and control, discrete-event systems theory and applications, sensor/observation networks, nuclear safeguards and nonproliferation technologies, surveillance and knowledge systems, online monitoring and diagnosis systems, artificial intelligence methods, fault/attack tolerant and reconfigurable hybrid systems, control systems security, decentralized monitoring and control, and operations and production management methods. He has over 45 publications and one U.S. patent.