An entropy-based sensor selection algorithm for structural damage detection

In this paper an experimental setup for structural damage detection is considered and a novel sensor selection algorithm is derived, based on the concepts of entropy and information gain from information theory, to reduce the number of sensors without affecting, or even improving (as happens in our experimental setup), model accuracy. An experimental dataset is considered showing that our method outperforms previous approaches improving the prediction accuracy and the damage detection sensitivity while reducing the number of sensors.