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A SHM method is proposed that minimises the required number of sensors fordetecting damage. The damage detection method consists of two steps. In aninitial characterization step, substructuring approach is applied to thehealthy structure in order to isolate the substructures of interest and later,each substructure is identified by a Vector Auto Regressive with eXogenousinputs (VARX) model measuring all DOFs. Then, pairwise conditional Grangercausality analysis is carried out with data measured from substructural DOFs toevaluate the information loss when measurements from all DOFs are notavailable. This analysis allows selecting those accelerometers that can besuppressed minimising the information loss. In the evaluation phase, vibrationdata from the reduced set of sensors is compared to the estimated data obtainedfrom the healthy substructure's VARX model, and as a result a damage indicatoris computed. The proposed detection method is validated by finite elementsimulations in a lattice structure model.

A SHM method for detecting damage with incomplete observations based on VARX modelling and Granger causality