Abstract
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.
Highlights
The placement of sensors significantly affects the performance of identification, state estimation as well as fault detection and isolation (FDI) algorithms
The network topology of the studied Heat Exchanger Networks (HENs) is shown in Following the detailed analysis of the problem with regard to the placement of sensors in the HEN, three other dynamical systems will be analyzed to illustrate the applicability of the methods on larger examples
Additional sensors are placed into the system based on the CLASA algorithm by the modified CLASA algorithm (mCLASA)
Summary
The placement of sensors significantly affects the performance of identification, state estimation as well as fault detection and isolation (FDI) algorithms. The goal-oriented placement of sensors for dynamical systems is a challenging task [1]. Parameter estimation-oriented information entropy-based optimal sensor placement was investigated in [2]. Based on robust information entropy, a Bayesian sequential sensor placement algorithm for multi-type of sensor is proposed [3]. Computational Fluid Dynamics (CFD) models were generated to predict wind-flow that used the data of a sensor placement utilised with prediction-value joint entropy [4]. For fault detection and isolation, an incremental analytical redundancy relation (ARR)-based algorithm was introduced in [5]
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