Sensor assignment for observability in interpreted petri nets

Aguirre-Salas, L.; Begovich, O.; Ramírez-Treviño, A.

doi:10.1016/s1474-6670(17)30786-3

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Among population-based optimization algorithms, genetic algorithms (GAs) are the most frequently applied, e.g., GA was utilized to find a sensor configuration that minimizes cost and maximizes the reliability and observability of the system [ 25 ]. The main benefit of these gradient-free heuristic optimization methods is that they can be utilized with a wide range of models [ 26 ]. Simulated annealing (SA) is also widely employed optimization algorithm in sensor placement [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Network Distance-Based Simulated Annealing and Fuzzy Clustering for Sensor Placement Ensuring Observability and Minimal Relative Degree

Leitold

Vathy-Fogarassy

Abonyi

2018

Sensors

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Network science-based analysis of the observability of dynamical systems has been a focus of attention over the past five years. The maximum matching-based approach provides a simple tool to determine the minimum number of sensors and their positions. However, the resulting proportion of sensors is particularly small when compared to the size of the system, and, although structural observability is ensured, the system demands additional sensors to provide the small relative order needed for fast and robust process monitoring and control. In this paper, two clustering and simulated annealing-based methodologies are proposed to assign additional sensors to the dynamical systems. The proposed methodologies simplify the observation of the system and decrease its relative order. The usefulness of the proposed method is justified in a sensor-placement problem of a heat exchanger network. The results show that the relative order of the observability is decreased significantly by an increase in the number of additional sensors.

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Section: Introductionmentioning

confidence: 99%