Abstract
Many industrial processes are inherently distributed in space and time and are called spatially distributed dynamical systems (SDDSs). Sensor placement affects capturing the spatial distribution and then becomes crucial issue to model or control an SDDS. In this study, a new data-driven based sensor placement method is developed. SVR algorithm is innovatively used to extract the characteristics of spatial distribution from a spatiotemporal data set. The support vectors learned by SVR represent the crucial spatial data structure in the spatiotemporal data set, which can be employed to determine optimal sensor location and sensor number. A systematic sensor placement design scheme in three steps (data collection, SVR learning, and sensor locating) is developed for an easy implementation. Finally, effectiveness of the proposed sensor placement scheme is validated on two spatiotemporal 3D fuzzy controlled spatially distributed systems.
Highlights
Many industrial processes are inherently distributed in space and time, such as fluid flow process, spray deposition process, heat exchange process, and snap curing process
Support vector regression (SVR) algorithm is used to extract the main characteristics of spatial distribution from a spatiotemporal data set, which can be directly used for the sensor placement
The sensor placement for an unknown nonlinear spatially distributed dynamical systems (SDDSs) is necessary for various applications
Summary
Many industrial processes are inherently distributed in space and time, such as fluid flow process, spray deposition process, heat exchange process, and snap curing process These systems are usually called spatially distributed dynamical systems (SDDSs) or distributed parameter systems (DPSs) [1]. On the research of SDDS, since sensor placement affects capturing the spatial distribution, it becomes one of key issues to influence the performance of spatiotemporal modeling or control. Zhang et al [15] proposed a sensor placement method for spatiotemporal 3D fuzzy control system based on spatial constrained fuzzy c-means algorithm. Support vector regression (SVR) algorithm is used to extract the main characteristics of spatial distribution from a spatiotemporal data set, which can be directly used for the sensor placement. The effectiveness of the proposed sensor placement scheme is validated on two spatiotemporal 3D fuzzy controlled nonlinear spatially distributed systems.
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