Optimal Sensor Configuration Design for Virtual Sensing in a Wind Turbine Blade Using Information Theory
Abstract
Optimal sensor placement (OSP) strategies in complex engineering systems aim to maximize the information gain from data by optimizing the location, type and number of the sensors or the actuators. It is used as a guide for assessing the structural condition, detecting damages and supporting the decision-making regarding structural health, safety and performance. In this work, a Bayesian optimal experimental design framework is used to optimize the type, location and number of sensors in composite wind turbine blades (WTB) excited by wind loads. The framework is based on a modal expansion technique for virtual sensing under output-only vibration measurements and on information theory for quantifying the information contained in a sensor configuration. The optimal sensor configuration optimizes a utility function associated with the expected Kullback-Leibler divergence (KL-div) between the prior and posterior distribution of the predictions at the virtual sensing (Ercan and Papadimitriou, Sensors 21:3400, 2021). The design variables include the location, type and number of sensors. Show more
Publication status
publishedExternal links
Editor
Book title
Model Validation and Uncertainty Quantification, Volume 3Journal / series
Conference Proceedings of the Society for Experimental Mechanics SeriesPages / Article No.
Publisher
SpringerEvent
Subject
Bayesian optimal experimental design; Information gain; Virtual sensing; Modal expansion; Wind turbinesOrganisational unit
03890 - Chatzi, Eleni / Chatzi, Eleni
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