Sensor configuration selection for discrete-event systems under unreliable 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 multiunit-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.

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Design of Supervisors for Active Diagnosis in Discrete Event Systems

2020

IEEE Trans. Automat. Contr.

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In recent years, with the development of computer science and electronic information technology, many large and complex systems have emerged. When a system runs online, it is inevitable to produce some faults. If a fault in a system cannot be detected and treated in time, some serious accidents may occur, resulting in economic and human losses. Therefore, in both academia and industry, fault diagnosis has received considerable attention. A discrete event system is a discrete-state and event-driven system. In modern society, discrete event systems have wide practical application backgrounds. Many systems can be represented by discrete event systems, such as flexible manufacturing systems, smart urban transportation systems and computer communication networks. In recent years, the problem of fault diagnosis in discrete event systems has received much attention. The problem of fault diagnosis in discrete event systems is to determine if some faults have occurred by observing events generated by a plant. On the other hand, diagnosability is a property of a plant such that any fault can be detected within finite future steps after its occurrence. In practice, a plant should necessarily be diagnosable since any fault that has occurred must be detected and be repaired in time to guarantee its safety and reliability. This thesis considers two discrete event modeling formalisms and studies a problem of active diagnosis: it consists in modifying an undiagnosable plant's behavior by supervisory control, thus ensuring that the closed-loop system is diagnosable. The main results of this thesis are summarized as follows.

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Sensor configuration selection for discrete-event systems under unreliable observations

Cited by 4 publications

References 24 publications

Agent-based system identification for control-oriented building models

Agent-based system identification for control-oriented building models

Selecting observation platforms for optimized anomaly detectability under unreliable partial observations

Design of Supervisors for Active Diagnosis in Discrete Event Systems

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