Characterization of identification errors and uses in localization of poor modal correlation

In a second part, test/analysis correlation is addressed with a focus on the localization of sources of poor mode shape correlation. The MACCo algorithm, which sorts sensors by the impact of their removal on a MAC computation, is shown to be particularly relevant. Combined with the error it avoids keeping erroneous modal components. Applied after removal of poor modal components, it provides spatial maps of poor correlation, which help localizing mode shape correlation errors and thus prepare the selection of model changes in updating procedures.