Measurement point selection in damage detection using the mutual information concept

The problem for measurement point selection in damage detection procedures is addressed. The concept of average mutual information is applied in order to find the optimal distance between measurement points. The idea is to select the measurement points in such a way that the taken measurements are independent, i.e. the measurements do not `learn' from each other. The average mutual information can be utilized as a kind of an autocorrelation function for the purpose. It gives the average amount of information that two points `learn' from each other. Thus the minimum of the average mutual information will provide the distance between measurement points with independent measurements. The idea to use the first minimum of the average mutual information is taken from nonlinear dynamics. The proposed approach is demonstrated on a test case. The results show that it is possible to decrease significantly the number of measurement points, without decreasing the precision of the solution.